3. PHYSICO - CHEMICAL PARAMETERS OF WATER

3.1 Introduction

Water serves as a natural renewable resource, a universal solvent and one of the most

precious commodities required for the survival of life. Basically, it is used for various purposes

like agriculture, forestry, urbanization and many other activities which satisfy human needs. The

water quality of a system depends on the terrain through which it flows and its quality depends

on physical, chemical and biological constituents (Honda, 1986). Various kinds of natural and

man made activities like industrial, domestic and agriculture create water pollution problems

participating in freshwater systems. According to WHO (1993) 30-80% of human diseases occur

by using impure water. The chemistry of water is influenced by the inputs of material containing

minerals and the chemical equilibrium which prevailing in the aqueous solution. Extent of

pollution depends on rainfall pattern, depth of water, distance from the source of contamination

and soil properties. Water is not available in sufficient quantities for human consumption,

agricultural developments and industries because of pollution which is one of the most

ecological crises today (Chatterjee, 1994). The river systems are much affected due to

undesirable unplanned urbanization and constructions which are harmful to the health of living

organisms including human beings (Sharma and Agarwal, 1999; Barunah and Baruah, 2003;

Kumar, 2003; Sumanatha and Saxena, 2004).

Water quality studies of any aquatic ecosystem are fundamental to understand the water

resource and one of the important features of the water body is the way in which they interact

with the surrounding land, particularly due to the agricultural activities of man, construction of

dams, deforestation and domestic as well as the industrial inputs. Since the quality of water

affect aquatic lives in several ways, water must be of good quality for the health of all organisms.

Today the freshwater bodies have become polluted by the discharges of dumping off waste,

mixing of sewage (Swaminathan and Manonmani, 1997). The effects of untreated sewage affect

the primary productivity of the freshwater environment (Sathiadas, 2002). Organic waste and

human interference in the Ahar river at Udaipur, drastically affect the water qualities (Sharma et

al., 2006). Kumar and Bahadur (2009) have pointed out the pollution potential of river Kosi at

Rampur. Pollution status of river Varuna in Varanasi, a tributary of river Ganga which flows in

the eastern part of Uttar Pradesh was studied by Singh and Anil (2009).

River plays a major role on integrating and organizing the landscape and moulding the

ecological setting of a basin. They are the prime factor controlling the water cycle and the most

dynamic agent of transport. Monitoring the surface runoff on river in a regular basis provides

valuable information on the eco-hydrological conditions. Such data helps to measure the health

of a river. The quality and quantity of surface water in a river basin is influenced by natural

factors such as rainfall, temperature, weathering of rocks and anthropogenic changes that curtail

natural flow of the river or alter its hydrochemistry. Several researches have documented the

physico-chemical dynamics of water (Rajeswari et al., 2005). Compared to the northern rivers in

India, the southern rivers are less studied perhaps due to their smaller size and discharges. Most

of the peninsular Indian rivers are highly seasonal and shorter although they form an effective

water realm in their basin and major transport of sediment to the Arabian sea (Gupta and

Chakrapani, 2005). Subramaniyan (1983) documented the water chemistry of down stream

variations in the rivers flowing through Indo-Gangetic plains. Singh and Singh (2007)

documented the physical, chemical and biological parameters of river Ganga. Detailed studies

have been carried out in the physico-chemical characters of water of Yamuna (Singh et al., 2007)

and Gomti (Singh et al., 2005).

The water quality of Mahanadi river basin was pointed out by Panigrahy and

Roymahashay (2004). Singh and Shrihari (2007) documented the water quality of river

Netravathi at Karnataka. Jeevanadan et al., (2007) studied the hydrogeo-chemistry and ground

water quality of the Ponnaiyar river basin in Tamil Nadu. The physico-chemical parameters of

river Chittar at Courtallam was documented by Murugan et al., (2007).

The diversity of flora and fauna is determined by the physical, chemical and biological

parameters of the water quality as indicated by the eutrophic nature of aquatic ecosystems

(Bervoets et al., 1996). Sharma (1996) reported the water qualities in relation with community

structure of Mahakali river at Nepal.

Considerable work has been carried out in the water quality assessment of Indian rivers

(Dhanpakiam et al., 1999; Schulze, 2000; Gyananath et al., 2001; Christian and Sheng, 2003;

Brick et al., 2004; Soni et al., 2006). Several researchers reported the physico-chemical nature

of few river systems (Rao et al., 1990; Kakati and Bhattacharya, 1990; Pandey and Mishra,

1990; Pandy et al., 1992; Das and Sinha, 1994; Susan et al., 2005; Ramani et al., 2006; Tsay and

Holben, 2007; Beg and Ali, 2008). Ramaganga river at Moradabad Uttarpradesh was studied by

Pandey and Sharma (1998). Mishra and Tripathi (2000) and Pratibha and Sultana (2006) has

pointed out the changes in the community structures and primary productivity of river Ganga.

Smith (1987) studied the riverine water qualities in the United States. Bahnwart et al.,

(1999) studied the ecological changes of Warnow river system in Germany. Limnological studies

in developing countries were reported by Chergui et al., (1999). Lounaci et al., (2000) pointed

out the community structure and parameters related to the water quality of an Algerian stream. In

northeast Argentina, factors involved in the changes of aquatic ecosystem were highlighted by

Gagneur and Thomas (2001). Budambula and Mwarchiro (2006) analysed the physico-chemical

parameters of Nairobi river. Neal et al., (2006) analysed the water quality of Thomas river at

south-eastern England.

The river pollution, water quality management and ecotoxicity in Australia were reported

by Hodson (1986). Bindey et al., (1994) pointed out the effect of heavy metals and

environmental protection in Africa. Ghosh and Bean (1998) have studied the water quality

parameters of Kali river, India. Karim and Badruzzaman (1999) studied the modeling of

nutrients and dissolved oxygen in the water column of a river system in Bangaladesh. The

quality of river Yamuna was assessed by Singh and Roy (2003). Adeyemo (2003) pointed out the

consequences of pollution and degradation of aquatic environment in Nigeria. Fotedar and Loan

(2004) has reported the water qualities of Udaipur district. In Nandan district of Maharastra,

Power and Pulle (2005) studied the physico-chemical parameters of Pethwada dam. Adeyemo

(2006) highlighted the human impact on the river system of Nigeria. The different factors and

nutrient concentration of Ujani dam was pointed out by Gore and Pingle (2007). Amita and

Fotedar (2008) have pointed out the water qualities of Basanta river in Jammu and Kashmir.

The quality of water is determined by parameters like pH, EC, turbidity, NO3, total

hardness, Ca, Mg, PO4, SO4, DO, Cl, Fl, and BOD in different stations along the course of the

river. These parameters are useful to assess the quality of water (Sinha et al., 2009).

In this study, water quality of river Thambraparani was examined at eight different

stations as the river collects water from different sources like agricultural land, surface run off,

manmade activities and drainage systems along its course. The study mainly emphasizes to

assess the spatial and temporal changes of water from the point of origin till the end of river

system.

3.2 Materials and Methods

Surface water samples were collected from the eight selected stations of the

river at monthly intervals for a period of two years from January 2007 to December 2008. The

collections were made between 6 am to 8 am by using clean sample bottles.

Parameters like nitrate and phosphate were estimated by photometric method. Sodium,

potassium, calcium, magnesium, iron, chloride and total alkalinity were analysed by standard

method of APHA (1998).

3.2.1 Temperature

Temperature is measured in the field by immersing thermometer in proper depth.

3.2.2 pH

A known quantity of water was scooped from the surface of the river in a glass bottle,

transported to the laboratory without much agitation. The pH was measured immediately by

using the pH meter.

3.2.3 Dissolved oxygen (Wrinkler's Iodometric method)

For the estimation of dissolved oxygen, water samples were collected in 250 ml

reagent bottles and fixed with alkaline iodide and manganous sulphate. For further estimation

they were brought to the laboratory.

3.2.4 Biological Oxygen Demand (Wrinkler's Iodometric method)

Water is collected from the river by BOD bottles and after five days of incubation at

20oC. The water was titrated by using Wrinkler's Iodometric method.

3.2.5 Estimation of Nitrate (Phenoldisulphonic acid method)

A known volume of the water sample was treated with phenol disulphonic acid (1

phenol, 2: 4 disulphonic acids) and the amount of nitrate reacted with it to 6 nitro 1, 2, 4

phenoldisulphonic acid. The sodium or potassium salt contained in it produces a yellow

colour whose intensity can be measured by calorimeter.

3.2.6 Estimation of phosphate (Vanadomolybdophosphoric acid calorimetric method)

In a dilute orthophosphate solution, ammonium molybdate reacts under acid conditions to

form a heteropoly acid, molybdophosphoric acid. In the presence of vanadium, yellow

nandomolybdophosphoric acid is formed. The intensity of the yellow colour is proportional to

phosphate concentration.

3.2.7 Estiamtion of potassium

Potassium can be determined in either a direct-reading or internal standard type of flame

photometer at a wavelength of 766.5 nm.

3.2.8 Determination of calcium and magnesium

Making use of ethylene diamine tetraacetic acid (EDTA) the concentration of calcium

and magnesium is estimated.

3.2.9 Estimation of sodium (Flame photometry)

Sodium emits a bright yellow colour when exited in the flame. The intensity

of emission is proportional to the concentration of sodium in the sample by flame photometer.

3.2.10 Estimation of chloride (Argentometric method)

The chloride present in the water is precipitated as silver chloride by titration with

standard silver nitrate solution using potassium chromate as the indiactor. After all the

chloride is precipitated, the excess of silver nitrate combines with potassium chromate indicator

to form flesh red precipitate of silver chromate.

3.2.11 Estimation of fluoride

The fluoride concentration of the water collected in the beaker with time. The experiments were

conducted using a column packed with 250 g of clay powder. The initial volume of water added

to the column was50 mL and its fluoride concentration was CFc.

3.2.12 Estimation of sulphate

The determination of sulphate concentration I water by indirect EDTA method.The water sample

is treated with excess barium chloride to precipitate Sulphate ions.The unpecipitated barium

iorns are then titrated with EDTA.

3.2.13 Estimation of alkalinity

Hydroxyl ions present in a sample as a result of dissociation of hydrolysis of

solutes react with additions of standard acid. Alkalinity thus depends on the end pH used.

Descriptive statistics for evaluating physico-chemical properties of water were used

for each sample taken from different study stations of the river. Replicates were

averaged to determine the mean value and utilized for data analysis with ANOVA and

correlation (Zar, 1996). The significance level was observed at P

3. 3. Results

3.3.1. Temperature

The monthly variation of surface water temperature at Thambraparani river during the

year 2007 is given in Fig 4, in which a minimum temperature of 18ºC was recorded in station 1

(S1) during December and a maximum of 24ºC was recorded in May. In S2, S4 and S5 the

lowest temperature observed was 18ºC in June and, the highest temperature, 24ºC in April.

Station 3 showed minimum temperature of 17ºC (June) and a maximum of 24ºC (May). In

station 6, the temperature ranged from 18oC (June) to 24

oC (May). A minimum temperature of

20ºC (June), and a maximum of 26ºC (April) was observed from station 7. Meanwhile, the

estuarine station (S8) showed an easily distinguishable value of 23oC (June) to 31

oC (August).

The annual mean values of temperature (Table 1) in the sampling stations were in descending

order as S8> S7> S6> S5> S4> S2> S3> S1.

The seasonal variation of water temperatures were high during pre-monsoon at station 8

(29.25) whereas, the low values (19.59) were observed during post-monsoon season at

station1 (Table 3).

Statistical analysis of two way ANOVA indicated a significant result between

seasons ( F = 46.76, p >0.05) and stations ( F = 28.85, p >0.05) (Table 5). Correlation co-

efficient shows negative significant observation with turbidity (r = - 0.52312 p < 0.05) (Table

37).

The variation in water temperature during the year 2008 is provided in Fig. 5. The

lowest temperature recorded was 180C in June at stations S2, S4, S5, and S6 and at S1 in

December. In S3, S7 and S8, the minimum temperature was observed in June, as 17ºC, 20ºC and

23ºC respectively. The highest of 22ºC at S1 was observed during April, August and September.

In S2 and S3 the peak value noticed was 23ºC (April, August, February and May) whereas, in

stations S4, S5 and S7 the highest readings were observed (24ºC, 28ºC and 25ºC respectively)

during May. 25ºC (April) was the highest value reported from station 6. But in S8 the maximum

temperature (31ºC) was noticed during August. The annual mean temperatures (mean±S.D.) of

different stations are shown in Table 2.

During the pre-monsoon season, the seasonal mean values of temperature was high in

the station 8 (29.25) whereas, the low value (19.5) was recorded during post-monsoon season of

the year 2008 (Table 4).

Statistical analysis of two way ANOVA in the year 2008 (Table 6) indicates the

variation of temperature is influenced by the seasons ( F = 39.80, p >0.05) and stations

significantly (F = 30.31, p > 0.05). Correlation co-efficient also shows positive significant

relationship with pH, (r = 0.865), E.C (r= 0.769), chloride (r = 0.8443), sulphate (r = 0.687),

alkalinity (r = 0.682), calcium (r =0.823) and Hardness (r = 0.865) (Table 38).

3.3.2. pH

The monthly variation of pH recorded during the year 2007 is shown in Fig 6. In station

1(S1), the minimum value observed was 6.52 in June. Mean while, in stations S2, S3 (February)

and S4 (June) the lowest value was 6.50. The minimum pH value of 6.63 and 6.80 were reported

from S5 and S6 during May and December respectively. But in station 7 and 8 the reported

lowest values were 6.60 and 7.9 (June). The highest value of pH was 8.0 (April) at S1, 8.40

(March) at S2, 8.20 (April) at S3, 7.40 (April) at S4, 7.18 (December), 7.30 (April) at S6, 8.10

(March) at S7 and 8.03 (April) at station 8. The highest and lowest annual mean values were 7.70

± 0.46 and 6.89 ± 0.21 in station 8 and 3 respectively (Table 1).

The seasonal mean value of pH was high during pre-monsoon (8.01), at station 8 in

the year 2007 and low (6.7) during monsoon season in station 4 (Table 3).

The statistical analysis by two way ANOVA indicates a significant observation

between seasons ( F = 57.65, p > 0.05) and stations ( F = 19.86, p >0.05) (Table 7) and also a

positive significant correlation with BOD ( r = 0.611) and negative significant correlation with

chloride (r = -0.613), alkalinity (r = - 0.540), turbidity (r = - 0.711) and DO ( r = -0.673) were

observed (Table 37).

The monthly variation of pH recorded during 2008 is shown in Fig.7 and it varied from

7.2 (January) to 8.0 (April) at S1 from 6.7 (June and August) to 8.40 (March) at S2, from 6.60

(June and August) to 7.20 (February and April) at S3, from 6.50 (June) to 7.75 (October) at

S4, from 6.65 (August) to 7.63 (May) at S5, from 6.75 (January) to 7.30 (April) at S6, from 6.60

(February, June and July) to 8.00 (March) at S7 and from 6.85 (July) to 8.1 (May) at station 8.

The annual mean pH values recorded from different stations are shown in Table 2. The peak

value was noticed at S8 (7.87± 0.28) against the lowest value of S4 (6.89± 0.29).

The seasonal mean observation of pH was high during post-monsoon at station 7 (8.55)

and low (6.53) during monsoon season of the year in station 1 (Table 4).

Statistical analysis by two way ANOVA indicated a significant result between seasons (F

= 31.99, p > 0.05) and stations (F = 2.25, p < 0.05) (Table 8). By correlation a positive

significant deviation with EC (r = O.636; p > 0.05), chloride (r = O.7.56; p > 0.05), sulphate (r =

O.522; p > 0.05), calcium (r = O.732; p < 0.05), hardness (r = O.775; p > 0.05) and temperature

(r = O.865; p > 0.05) were recorded and it also showed a negative significant correlation with

DO ( r = -0.555; p < 0.05) only (Table 38).

3.3.3. Turbidity

Monthly fluctuations of turbidity recorded at different stations during the period of

investigation are given in Fig.8. The lowest turbidity value of 4 NTU was reported from the

station S1during April. In March, a minimum of 5 NTU was noted from the stations S2 and S7.

In stations S4, S5, S6 and S8, the lowest values observed were 6 NTU during the months of

March, February and December. The highest values of 28, 25 and 24 NTU were recorded during

August from stations S3, S7 and S2 respectively. In station S5, highest turbidity recorded was 22

NTU (October). In June, the peak values (21, 20 and 19 NTU) were observed from S1, S4, and

S8 respectively. The lowest of 16 NTU was reported from station 6.The annual mean values

ranged from 11.08 ± 2.96 at S8 to 15.5 ± 7.41at S3 (Table 1).

The seasonal mean values of turbidity was high during monsoon season in station 3

(23.5) and low during pre-monsoon season (4.0) of the year (Table 3).

Statistical analysis (Two way ANOVA) revealed that, data on seasonal variation of

turbidity showed a significant deviation between seasons (F = 62.90, p > 0.05) and it was non-

significant between stations (Table 9). A positive significant correlation was reported (Table 37)

with chloride (r = 0.739), fluoride (r = 0.522), sulphate (r = 0.610), alkalinity (r = 0.624),

magnesium (r = 0.726), calcium (r = 0.605) and hardness (r = 0.509). It shows negative

significant result with pH (r = -0.711) alone.

In the following year 2008 the observed values of turbidity are shown in Fig. 9. Among

the eight experimental stations minimum turbidity of 2 NTU was recorded at S1 in April and

May, at S2 in March. 3 NTU was observed in S4 (March) and S8 (December). In the remaining

stations lowest values noticed were in an order of 8 NTU in S3 (May), 7 NTU in S5 (January), 5

NTU in S6 (December), and 4NTU in S7 (October). The Highest values noted in these stations

were 28 NTU in S3 (August), 25 NTU in S1and S7 (June and August), 24 NTU in S2 (August),

23 NTU in S4 (June), 21 NTU in S5 (October), 17 NTU in S8 (September) and 16 NTU in S6

(September).The highest annual turbidity of 15.91 ± 6.51 was recorded at S3 and the lowest

mean of 11.08 ± 2.96 was at S6 (Table 2)

The seasonal fluctuation of mean values (Table 4) of turbidity were recorded high (23.0)

during monsoon at station 3 and low during pre-monsoon season (6.25) at station 2

Statistical analysis based on two way ANOVA indicated a significant result

between seasons (F = 22.83, p >0.05) and a non significant observation between the stations (F

= 0.66, p <0.05) (Table 10). A positive significant correlation (Table 38) with phosphate (r =

0.567; p > 0.05) and iron (r = 0.633; p > 0.05) was also recorded.

3.3.4. Electrical Conductivity (EC)

The monthly variation of electrical conductivity values recorded in the Thambraparani

river during the year 2007 and 2008 are illustrated in Fig 10 and 11. In this river, a minimum of

39mMhos/cm 2 was observed at station 1 in January and the maximum of 1190 mMhos/cm 2

was reported in June at station 8. The annual mean (mean± S.D.) of different stations are shown

in table 1.

The electrical conductivity values were higher at station 8(1103) during monsoon in the

year 2007 and lower during post-monsoon season (40.5) (Table 3).

The data on statistical analysis of two way ANOVA (Table 11) indicated a non

significant result between seasons (F = 1.77, p <0.05) and the variations were significant

between stations (F= 18.17, p >0.05). Correlation co-efficient (Table 37) showed positive

significant correlation with chloride (r = 0.624; p > 0.05), fluoride ( r = 0.663; p > 0.05), sulphate

( r = 0.541; p > 0.05), alkalinity (r = 0.606; p > 0.05) and calcium ( r = 0.665; p > 0.05) and

negative significant with nitrate ( r = -0.520; p < 0.05) only.

In 2008, low values of electrical conductivity were noticed in December at stations 1, 2

and 3 and in the ascending order (˂40mMhos/cm2, ˂50mMhos/cm

2 and ˂60mMhos/cm

2)

respectively. The stations S4, S5, S6, S7and S8 showed the values of 56mMhos/cm2

(May),

72mMhos/cm2 (January), 40mMhos/cm

2 (October), 50mMhos/cm

2 (January and November) and

380 mMhos/cm2 (October) respectively. The highest values of 1190 mMhos/cm

2,

123mMhos/cm2

and 100mMhos/cm2

were observed in June at stations S8, S7, and S5. In S1 the

peak value (150mMhos/cm2) was recorded in September. During February the electrical

conductivity value reached upto 75mMhos/cm2 at station 2. In S3 and S4 the maximum values

(82mMhos/cm2 and 85mMhos/cm

2) were noted during the months of August and October. The

annual mean values of the sampling stations occur in descending order as S8> S7> S5> S6> S4>

S3> S1> S2 (Table 2).

The seasonal observation on electrical conductivity (1103.5) of station 8 was high during

monsoon and low (44.25) during post-monsoon season in station 1(Table 4).

Two way analysis of variance indicated a significant influence between seasons only (F=

19.52, p >0.05) (Table 12). A positive significant correlation with chloride ( r = 0.954),

sulphate ( r = 0.953), alkalinity ( r = 0.955), calcium ( r = 0.957), hardness ( r = 0.948) and

temperature ( r = 0.769) were observed (Table 38).

3.3.5. Total hardness

The data on the monthly variation of total hardness at Thambraparani river in the

year 2007 is presented in Fig 12. The values ranged from a minimum of 9 mg/L (December)

to the maximum of 17 mg/L (September) at S1, from 10 mg/L (December) to 22 mg/L

(June) at S2, from 13 mg/L (January) to 26 mg/L (June) at S3, from 12 mg/L (January)

to 29 mg/L (September) at S4, from 16 mg/L (January) to 46 mg/L (August) at S5, from 19

mg/L (November) to 38 mg/L (June) at S6, 22 mg/L (November) to 52 mg/L (September) at S7

and 96 mg/L (January) to 146 mg/L (August) respectively. The highest (118 ± 16.76) and lowest

(12.66 ± 2.34) annual mean were recorded at station 8 and 1 respectively (Table 1).

Seasonal mean values of total hardness obtained were high during monsoon (145.2) at

station 8 and low (11.5) during pre-monsoon season at station 1 (Table 3).

The two way ANOVA (Table 13) produced a statistically significant result between

seasons (F = 46.49, p >0.05) and stations (F= 872.17, p >0.05). A positive significant

correlation was obtained (Table 37) with chloride ( r = 0.670), sulphate ( r = 0.594), alkalinity (

r = 0.678), magnesium ( r = 0.597), turbidity ( r = 0.605) and dissolve oxygen ( r = 0.757). A

negative significant result with biological oxygen demand was noticed (r = -0.840; p < 0.05).

Variation of total hardness of water in the year 2008 is shown in Fig. 13. The minimum

values (7 mg/L, 8 mg/L, and 18 mg/L) were recorded at stations S1, S2 and S6. The stations S3,

S7 and S8 showed the low values (13 mg/L, 25 mg/L, and 90 mg/L) during January. In

November, 14 mg/L and 18 mg/L were noted as the lowest values from stations S4 and S5.

Meanwhile, the highest values reported from the experimental stations, 1 to 8 were 18 mg/L

(July), 22 mg/L (June), 27 mg/L (July), 30 mg/L (October), 45 mg/L (August), 38 mg/L (June),

56 mg/L and 160 mg/L (August). The annual mean values ranged from 11.75 ± 3.62 at S1 to

125.33 ± 24.66 at S8 (Table 2).

The observation on the seasonal changes of water hardness were recorded high during

monsoon season at the station 8 (131.75) in the year 2008 whereas the low values (10.25)

during post-monsoon season of the same year (Table 4).

Two way ANOVA indicated a significant influence between seasons (F = 24.68, p >0.05)

and stations (F= 363.00, p >0.05) (Table 14). The correlation co-efficient showed positive

significant observation with pH ( r = 0.775; p > 0.05), EC (r = 0.948; p > 0.05), chloride ( r =

0.966; p > 0.05), sulphate ( r = 0.899; p > 0.05), alkalinity ( r = 0.881; p >0.05), calcium ( r =

0.988; p >0.05) and temperature ( r = 0.823; p >0.05) (Table 38).

3.3.6. Alkalinity

The total alkalinity values noted in different stations during the present investigation for

the year 2007 is shown in Fig.14. The minimum values were reported in December (S1, S2, S3,

S6 and S8) and January (S4 and S5). But, in station 7 the lowest value (8mg/L) recorded was in

November. The peak values were obtained in the month of July and September in the stations S1

(28 mg/L) S2 (21 mg/L) and S6 (21 mg/L), S5 (28 mg/L), S7 (22 mg/L), and S8 (85 mg/L). In

June (27 mg/L) and August (24 mg/L) the concentrations of alkalinity reached its maximum in

stations S3 and S2. The annual mean values are shown in Table1 and maximum of 51.66 ±16.76

reported in station 8

The seasonal mean values reported were high during monsoon (84.5) in station 8 and

low (7.5) in S3during post-monsoon season (Table 3) .

Two way analysis of variance (Table, 15) indicated a significant result between seasons

and stations (F = 21.60 and 23.56, p >0.05). A positive significant correlation was obtained

(Table 37) with electrical conductivity ( r = 0.606; p >0.05), chloride ( r = 0.962; p > 0.05),

fluoride ( r = 0.751; p > 0.05), sulphate ( r = 0.949; p >0.05), calcium ( r = 0.866; p > 0.05),

hardness( r = 0.678; p > 0.05), turbidity ( r = 0.624; p >0.05),dissolve oxygen ( r = 0.847; p >

0.05) and negative significant with pH ( r = -0.540; p < 0.05), and nitrate only ( r = -0.614; p

<0.05).

In the following year 2008, the monthly variations in alkalinity values from different

stations are given in Fig 15. In stations S1, S2, S3, S6, S7 the minimum alkalinity values of 7

mg/L, 6 mg/L, 5 mg/L, 6 mg/L and 7 mg/L were recorded during December. While in S4, S5 and

S8 the minimum values (6 mg/L, 6 mg/L and 17 mg/L) were noticed in January. But high

alkalinity values was reported during August at stations S2 (25 mg/L), S3 (24 mg/L), and S8 (90

mg/L). In July, the station S1 (28 mg/L) and S6 (23 mg/L) showed the maximum alkalinity. In

stations S5 and S7 the highest values (27 mg/L and 26 mg/L) were reported in September,

whereas 30 mg/L was the highest value attained in station 4 during June. The annual mean

(Table 2) values of the sampling stations occur in the order as S8> S5> S4> S7> S6> S3> S2>

S1.

As noticed in the previous year of study, the seasonal mean values reported were

maximum during monsoon (78.25) at station 8 and minimum (7.0) during post-monsoon

season of the experimental station 2 (Table 4).

Statistical analysis by two way ANOVA (Table 16) indicated a significant deviation

between seasons and stations (F = 15.92 and 21.40, p > 0.05). Results on correlation co-

efficient indicated a positive significant obsevation with electrical conductivity (r = 0.955),

sulphate (r = 0.978), calcium (r = 0.915), hardness (r = 0.881) and temperature (r = 0.682). A

negative significant correlation with nitrate alone (r = -0. 553; p < 0.05) was also reported.

3.3.7. Dissolved Oxygen (DO)

The dissolved oxygen content recorded during the year 2007 is given in Fig 16. Out of

the 8 stations, stations S2 (3.61 mg/L), S4 (8.44 mg/L) and S5 (9.38 mg/L) showed minimum

DO level during the month of May. In stations, S3 (7.16 mg/L) and S1 (4.32 mg/L), reported low

DO level in February. In the remaining stations, S6 (8.24mg/L), S7 (6.58 mg/L) and S8 (3.53

mg/L), the lowest DO level was recorded during November, April and March respectively. In

stations S2 (11.02 mg/L), S3 (10.65 mg/L), S4 (11.19mg/L), S5 (13.28 mg/L) and S8 (6.82

mg/L), highest values were noted in June. In the other stations, S1 (7.51 mg/L), S6 (10.13mg/L),

and S7 (10.18 mg/L) maximum values were found in September. The annual mean values were

high (9±1.13) in station 4 and low in station 3 (Table 3)

The data on seasonal mean values of dissolve oxygen content (Table 3) recorded were

higher (10.65) (S4) during mon-soon and lower during pre-monsoon season (7.03), of the year

2007.

The statistical analysis (Two way ANOVA) indicated a high significant influence of DO

between seasons (F = 16.8, p >0.05) only (Table 17). Correlation co-efficient showed a positive

significant result with chloride (r = 0.847), fluoride (r = 0.552), sulphate (r = 0.810), alkalinity (r

= 0.847), magnesium (r = 0.662), calcium (r = 0.757), hardness (r = 0.526), turbidity (r = 0.744)

and it was negative significant with pH (r = -0.673; p < 0.05) only (Table 37).

In the following year 2008, dissolved oxygen content varied from 5.82 mg/L (March)

to 7.84 mg/L (August) at S1, from 7.5 mg/L (May) to 11.03 mg/L (June) at S2, from 8.52

mg/L (March) to 10.67 mg/L (July) at S3, from 8.5 mg/L (May) to 11.20 mg/L (June) at S4,

from 9.32 mg/L (May) to 11.75 mg/L (July) at S5, from 6.9 mg/L (April) to 11.0 mg/L (August)

at S6, from 7.51 mg/L (April) to 10.09 mg/L (July) at S7 and 3.51 mg/L (March) to 6.94 mg/L

(June) at S8 (Fig.17). The annual mean values ranged from 8.26 ± 1.17 at S8 to 9.19 ± 1.29 at S4

(Table 2).

The seasonal mean values of dissolve oxygen content was high were higher during

monsoon (10.23) in S4 and low during pre-monsoon season (6.87) of the stations studied (Table

4).

Statistical analysis of the data (Two way ANOVA) indicated a significant observation

between seasons (F = 24.5, p >0.05) only (Table 18). A positive significant result with turbidity

(r = 0.725) and a negative significant correlation with BOD (r = -0.712; p <0.05), and pH (r = -

0.555; p < 0.05) was obtained (Table 38).

3.3.8. Biological Oxygen Demand (BOD)

The monthly variation of biological oxygen demand recorded from Thambraparani

river during the year 2007 is given in Fig18. A low level was recorded at station S1 (0.91

mg/L), S4 (1.12 mg/L) and S6 (0.54 mg/L) in June. During July and September, the low values

were reported from S2, S5, S7 and S3. But in station 8 the values remain very low (0.1mg/L) in

August. Meanwhile, maximum values were reported during March in stations S3 (2.36 mg/L)

and S8 (2.53 mg/L). In the other stations also the BOD levels observed were very high in

September, October, November, December and February months. The annual mean values in the

sampling stations occur in the descending order of S1> S3> S6> S4> S2> S7> S5> S8 (Table 1).

During pre-monsoon and monsoon season at station 6, higher (2.72) and lower value

(0.73) at station 5 were recorded (Table 3).

The computation of the two way ANOVA test showed a significant influence of BOD

levels between seasons ( F = 8.44, p >0.05) and it was non significant between stations (Table

19) . A positive significant correlation was obtained with pH(r = -0.611; p > 0.05) only in S1 and

negative significant result was observed with chloride (r = -0.826; p < 0.05), sulphate (r = -0.680;

p < 0.05), alkalinity (r = -0.753; p < 0.05), magnesium (r = -0.677; p < 0.05), calcium (r = -

0.840; p <0.05) and turbidity (r = -0.762; p <0.05) (Table 37).

The biological oxygen demand level recorded in different stations of the year 2008 is

shown in Fig. 19. In S1, S4 and S6 the minimum biological oxygen demand values of 0.8mg/L,

0.96mg/L and 0.21mg/L were recorded in June. While, in S8 the minimum was 0.4 mg/L

(August). In S2 and S5 the lowest value (0.4 mg/L and0.07mg/L) was noted in July. In stations

S3 and S7, low values (0.94mg/L and 0.11 mg/L) in September were reported. High biological

oxygen demand values of 3.93 mg/L and 3.0 mg/L were observed during May in stations S1 and

S6. The highest values of 2.25 mg/L (December), 2.22 mg/L(January), 2.74 mg/L (February),

2.69 mg/L(March), 2.38 mg/L(April) were recorded in stations S4, S2, S5, S8, S7 respectively.

The annual mean values (Table2) ranged from 1.39 ± 0.74 to 1.92 ± 0.90 at S5 and S1

Seasonal observations indicated that maximum and minimum biological oxygen

demand levels (2.72 and 0.86) were recorded during pre-monsoon and monsoon season of the

year 2008 (Table 4).

Statistical analysis (Two way ANOVA) indicated that the variation in biological oxygen

demand was influenced significantly by the seasons only (F = 40.34, p > 0.05) (Table 20). It was

also reported that a negative significant results with dissolve oxygen (r = -0.712; p < 0.05) and

phosphate (r = -0.575; p < 0.05) by correlation co-efficient (Table 38).

3.3.9. Phosphate

The quantity of phosphate content recorded from the experimental stations during the

year 2007 is given in Fig.20. In stations S2, S3, S6 and S7 the minimum phosphate value

observed were 0.05 mg/L, 0.06 mg/L, 0.04 mg/L and 0.03 mg/L in January. Whereas, in stations

S4 and S5 minimum values (0.04 mg/L and 0.08 mg/L) were noted in October. But in S1 and S8

lowest values were reported during December (0.01 mg/L) and April (0.04) mg/L respectively.

Mean time, the highest values were noted during June in stations S1 (0.32 mg/L), S2 (0.65

mg/L), S5 (0.55 mg/L) and S6 (0.35 mg/L). In the month of July, stations S4 and S8 showed

highest values (0.20 mg/L and 0.3 mg/L). In August, the phosphate level of water reached its

peak values at stations S3 and S7. The annual mean phosphate content (mean± S.D.) of different

stations are shown in table 1.

Data on seasonal variations of phosphate content is shown in Table 3 and the mean

values were maximum during monsoon season (0.45) in S2 and minimum (0.02) during post-

monsoon season of the first year.

Data on the Two way analysis of variations indicated that, the phosphate concentrations

are influenced by seasons and stations (F = 64.62,) (F = 5.76, p >0.05) in the year 2007 (Table

21). On correlation co-efficient, a positive significant correlation was obtained with chloride (

r= 0.587), sulphate ( r= 0.597), alkalinity ( r= 0.529), iron ( r= 0.506), magnesium ( r= 0.591)

and calcium ( r= 0.609) (Table 37).

The monthly variation of phosphate content in the year 2008 is shown in Fig. 21. The

concentrations varied from 0.01 (December) to 0.32 mg/L (June) at S1, from 0.05 mg/L

(January) to 0.65 mg/L (July) at S2, from 0.05 mg/L (January) to 0.85mg/L

(December) at S3, from 0.04 (October) to 0.19 mg/L (September) at S4, from 0.08 mg/L

(November) to 0.55 mg/L (June and July) at S5, from 0.05 mg/L (October) to 0.35 (June) at S6,

from 0.04 mg/L (October) to 0.26mg/L (August) at S7 and from 0.1mg/L (February) to 0.5 mg/L

(May) at S8. At station 5 the highest (0.28 ± 0.18) annual mean value was observed against the

low value of 0.12 mg/L in station 1.

The seasonal fluctuation of phosphate content recorded is shown in table 4. Highest mean

values were recorded during pre-monsoon season in station 4 (0.46) and lowest values in

station 7 (0.04) during the post-monsoon season.

Statistical analysis of two way ANOVA indicated a significant relationship between

station ( F = 7.72, p >0.05)and seasons (F= 5.10, p > 0.05) in the year 2008 (Table 22). A

positive significant correlation (Table38) was obtained with nitrate (r= 0.631; p > 0.05) and

BOD ( r= 0.575; p > 0.05).It also showed and positive significant correlation with iron ( r =

0.580; p > 0.05) and turbidity ( r = 0.567; p > 0.05).

3.3.10. Nitrate

The monthly variation of nitrate concentrations varied from 7 mg/L (June) to 24

mg/L (October) at S1, from 7mg/L (June) to 16 mg/L (August) at S2, from 0.07 mg/L

(September) to 27mg/L (October) at S3, from 1 mg/L(June and August) to 7 mg/L (January) at

S4, from 3mg/L (September) to 20mg/L (October) at S5, from 1mg/L (July and September) to

42mg/L (January) at S6, from 11mg/L (March) to 62mg/L (October) at S7 and 1mg/L (July and

August) to 20mg/L (November) at S8 (Fig.22). The annual mean values are provided in Table1.

Station 7 showed highest mean values of 28.16 ± 16.91 against the low value of 3.08 ± 1.83 at

station 4.

Observation on seasonal variation indicated high nitrate concentrations during post-

monsoon (48.75) at station S6 and low concentration (0.07) at S1 during monsoon season of the

experimental stations (Table 3).

Data on statistical analysis (Table 23) of two way ANOVA indicated that nitrate content

was highly influenced by seasons and stations (F = 5.42, F = 6.05, p >0.05) (Table 23). A

positive significant correlation was obtained with EC ( r= 0.520; p < 0.05), alkalinity ( r =

0.614; p < 0.05), sulphate ( r= 0.512; p < 0.05), calcium ( r= 0.509; p < 0.05) and hardness (r =

0.727; p < 0.05) (Table 37).

The monthly nitrate concentration from different experimental stations in the year 2008 is

shown in Fig 23. It ranged from 2.0 mg/L to 42 mg/L in all stations and the annual mean

nitrate (mean± S.D.) of different stations are shown in table 2.

The seasonal fluctuation of nitrate concentration is shown in (Table 4). The highest

concentration was observed in station 7 (49.65) during post-monsoon season and lowest (0.09)

during monsoon season at station 3 during the year 2008.

It is inferred from the result of Two way ANOVA (Table 24) that, the variation in

nitrate content was influenced by seasons and stations ( F = 6.82, F = 5.87, p > 0.05).

Correlation co-efficient studies reported a positive significant result with phosphate ( r=

0.631; p > 0.05), sulphate ( r= 0.604 p > 0.05), alkalinity (r = 0.553; p > 0.05), iron (r = 0.624; p

> 0.05) and calcium ( r= 0.502; p > 0.05 respectively (Table 38).

3.3.11. Chloride

The variation of chloride content of Thambraparani river in the year 2007 is provided in

Fig.24. The values ranged from 3 mg/L (April and October) to 18 mg/L (July) at S1,

from 3 mg/L (November) to 18 mg/L (June) at S2, from 5 mg/L (December, January and

March) to 18 mg/L (August) at S3, from 4 mg/L (February) to 15 mg/L (August) at S4, from

9mg/L (January) to 23mg/L (September) at S5, from 12.7mg/L (October) to 38.0 mg/L (April) at

S6, from 10mg/L (December) to 28 mg/L (February and August) at S7 and from 6 mg/L

(January) to 17mg/L (April) at S8. The annual mean values in the sampling stations were

reported in descending order as S6> S7> S5> S8> S4> S3> S1> S2 (Table 1).

The seasonal variation in chloride content recorded was maximum during pre-monsoon

(35.07) in station 6 and minimum (4.25) in station 2, during post-monsoon season of the year

2007 (Table 3).

Two way ANOVA (Table 25) indicated a non-significant result between seasons ( F

= 0.08, p <0.05) and it influences significantly by the different experimental sites (F = 104.45, p

>0.05). A positive significant correlation with EC (r= 0.624; p > 0.05), fluoride (r= 0.796; p

>0.05), phosphate (r= 0.587; p > 0.05), sulphate (r= 0.915; p > 0.05), alkalinity (r= 0.962; p

>0.05), magnesium (r= 0.768; p >0.05), calcium (r= 0.888; p > 0.05), hardness (r= 0.670; p >

0.05), turbidity (r= 0.739; p > 0.05) and negative significant result was observed with pH ( r= -

0.613; p < 0.05) ,Table 37.

Data on monthly variation of chloride content of the year 2008 is shown in Fig. 25 which

ranged from 3 mg/L (April) to 17 mg/L (July) at S1, from 3 mg/L (December) to 16

mg/L (August) at S2, from 5 mg/L (January, March and May) to 18 mg/L (August) at S3,

from 4 mg/L (March) to 16 mg/L (July) at S4, from 9mg/L (February) to 23mg/L (August) at

S5, from 12.7mg/L (November) to 39.0 mg/L (July) at S6, from 10mg/L (November) to 23 mg/L

(September) at S7 and from 8 mg/L (September, October and November) to 16mg/L (July) at S8

(Fig. 24). The highest annual mean value of chloride content (26.32 ± 9.41) was recorded at

station 6 and the lowest mean values of 8.58 ± 5.38 was reported at station 1 (Table 25).

Data on seasonal variations of chloride content is provided in table 4. Highest mean value

was recorded during monsoon (29.65) and the lowest of 4.25 was reported during post-monsoon

at station 8 and 2 respectively.

Statistical analysis (two way ANOVA) indicated that, chloride content was not

influenced by seasons (F = 4.55, p <0.05) but significantly influenced by stations (F = 226.04, p

>0.05) in the year 2008(Table 26). Correlation co-efficient showed a positive significant

relationships with pH (r = 0.756; p > 0.05), EC (r = 0.954; p >0.05), sulphate (r = 0.893; p >

0.05), alkalinity (r = 0.897; p > 0.05), calcium (r = 0.962; p >0.05), hardness (r = 0.966; p > 0.05)

and temperature (r = 0.844; p >0.05) (Table 38).

3.3.12. Calcium

Data on the monthly variation of calcium concentration observed in the experimental

stations are provided in Fig.26. During January, the calcium level observed was minimum in S1

(2 mg/L), S2 (2 mg/L), S4 (3 mg/L), S6 (2 mg/L) and S7 (2 mg/L) respectively. In stations, S3 (2

mg/L), S5 (3 mg/L) and S8 (18 mg/L) lowest values were reported in October, December and

November. The highest values were obtained during June at S3 (7mg/L), S4 (14 mg/L), S5 (11

mg/L) and S6 (10 mg/L). In 3 stations, S1, S2, S7 the highest values (10 mg/L, 10 mg/L, 14

mg/L) were reported in July. But in S8, the highest value was (40 mg/L) recorded in August. The

annual mean values ranged from 5.66 ± 2.10 at S3 to 24.75 ± 5.56 at S8 (Table 1).

The seasonal variation in total calcium content observed is shown Table3. Highest (58.0)

and lowest mean values (3.5) were reported during monsoon and post-monsoon season at station

2 of the year 2007.

Two way variance test indicated that the variation of calcium was influenced significantly

by the seasons and stations (F = 32.75; F = 117.12, p > 0.05) in the year 2007(Table 27). A

positive significant correlation was reported with EC (r = 0.665; p > 0.05), chloride (r = 0.888; p

> 0.05), fluoride (r = 0.719; p > 0.05), phosphate (r = 0.609; p > 0.05), sulphate (r = 0.809; p >

0.05), alkalinity (r = 0.866; p > 0.05), magnesium ( r = 0.784; p >0.05), turbidity ( r = 0.605; p

>0.05), DO ( r = 0.757; p > 0.05) and negative significant result with nitrate ( r = -0.509; p <

0.05) and BOD ( r = -0.840; p <0.05) (Table 37).

The monthly variation of calcium observed in the experimental stations during the year

2008 is represented in Fig. 27. The lowest calcium content was observed in January from S1 (2

mg/L), S2 (2 mg/L), S6 (1 mg/L), and S7 (3 mg/L). In other stations the values were 9 mg/L

(S3), 4 mg/L (S4), and 3 mg/L (S5), 16mg/L (S8). The highest value reported in June at 3

stations, S2 (10 mg/L), S6 (14 mg/L) and S7 (12 mg/L). In S1 (8 mg/L) and S8 (36 mg/L), the

highest values were recorded in August. But, in S3 (10 mg/L), S4 (13mg/L) and S5 (12 mg/L)

maximum Ca level was observed in January, July and September respectively. The annual mean

values was recorded high (28.5 ± 6.86) at station 8 and low (4.50 ± 1.88) at station 3 (Table 2).

The seasonal mean data recorded was maximum during monsoon (28.5) and the

minimum of 0.35 mg/L was observed during post-monsoon season at station 8 and 5

respectively (Table 4).

Statistical analysis of two way ANOVA indicated that the variation of calcium was

influenced significantly by the seasons (F = 59.15, p > 0.05) and stations ( F = 133.26, p >

0.05) in the year 2008 (Table 28). A positive significant correlation with pH, EC, chloride,

sulphate, alkalinity, hardness and temperature, was reported. Moreover, a negative significant

correlation with nitrate was also observed (Table 38).

3.3.13. Magnesium

Data on the monthly variation of magnesium content of Thambraparani river during the

year 2007 is given in Fig.28. The monthly concentration varied from 0 mg/L to 38mg/L. The

annual mean values of the sampling stations occur in the descending order as S2> S6> S7> S8>

S4> S3> S5> S1 (Table 1).

The seasonal variation of magnesium observed from the experiment stations are depicted

in Table 3. During monsoon season the mean values reported was high (32.75) at station 2 and

low during post-monsoon (0.45) at station 5.

The data analyzed by two way ANOVA indicated a non significant result between

seasons ( F = 0.64, p <0.05) and stations ( F = 0.94, p <0.05) in the year 2007 (Table 29). A

positive significant correlation was reported with chloride (r = 0.768), fluoride (r = 0.567),

phosphate (r = 0.591), sulphate (r = 0.717), alkalinity (r = 0.740), calcium (r = 0.784), hardness(r

= 0.597), turbidity (r = 0.726) and DO. (r = 0.662) It also showed a negative significant result

with BOD (r = -0.677) alone (Table 37).

In the following year 2008, 1 mg/L was the lowest magnesium level recorded in the

different experimental stations (S1, S3, S4, S5, S6 and S8). The stations S2 (5 mg/L) and S7 (2

mg/L) alone showed a varied result. The stations S3, S4, S7 and S8 showed highest value of

5mg/L (June and August). 4mg/L as the highest level of Mg reported from S1, S5 and S6. Station

2 exhibits a drastically highest value (31 mg/L) apart from other stations during the month of

August (Fig.29). The annual mean value reached a peak level in station 2 (17.91 ± 11.68) and a

low level in station 1 (1.41 mg/L) during the study period (Table 2).

During monsoon, seasonal mean values were recorded higher (25.25) and the low

values (1.75) were at station 4 recorded during post-monsoon season in the year 2008 (Table

4).

Two way ANOVA indicated that, the variation of magnesium was not influenced by

seasons ( F = 2.83, p <0.05) but influenced by stations( F = 10.99, p >0.05) in the year 2008

(Table 30).

3.3.14. Fluoride

The monthly variation of fluoride content observed is shown in Fig. 30. The

concentrations varied from 0.1 mg/L (October and January) to 0.9 mg/L (August) at S1, from 0

mg/L (November and December) to 0.8 mg/L (July) at S2, from 0.1 mg/L (October, January,

February and April) to 0.9mg/L (September) at S3, from 0 mg/L (December) to 0.6 mg/L

(August) at S4, from 0.1mg/L (October and December) to 0.9mg/L (September) at S5, from

0.1mg/L (March) to 0.8mg/L (June) at S6, from 0mg/L (July) to 0.8mg/L (February) at S7 and

0.1mg/L (April) to 0.6mg/L (November) at S8 (Fig. 30). The highest annual mean of fluoride (48

± 0.24) was recorded at station 1 and lowest mean (0.26 ± 0.27) was at station 2 (Table 1).

Seasonal mean variations recorded during the study period is shown in table -3. During

monsoon the values (0.75) were high and low values (0.05) were recorded during post-

monsoon season in station 1 and 2 respectively.

The data analysis by two way ANOVA indicated that the variations of fluoride content

was not affected by seasons (F = 3.16, p <0.05) and stations ( F = 1.40, p <0.05) in the year

2007 (Table 31). Correlation co-efficient showed a positive significant observation with EC,

sulphate, alkalinity, magnesium, calcium, turbidity and DO (p >0.05) (Table 37).

The monthly variation of fluoride content of recorded different stations of the river in the

year 2008 is shown in Fig. 31. In October, fluoride level was lower in 2 stations, S1 (0.1 mg/L)

and S2 (0.0 mg/L). In S3, the lowest value (0.1 mg/L) was reported in January, February and

April. In January, lowest value (0.2 mg/L) was again noted in 3 stations, S4, S5 and S6. But in

station S7 and S8 the lowest value of 0.0 mg/L and 0.5 mg/L was reported in August and

November. 0.9 mg/L was the high fluoride level reported in 4 stations, S1 (September), S2

(July), S3 (August), and S5 (September). In August, highest value was observed in stations S4

(0.6 mg/L) and S6 (0.16 mg/L). In stations S7 and S8, a same highest value, 0.5 mg/L was

observed in September and November respectively. The annual mean values ranged from 0.21 ±

0.20 at S2 to 0.50 ± 0.26 at S1 are provided in table 2.

During monsoon and post-monsoon season the maximum (0.75) and minimum (0.15)

values were observed at station 1 and 2 respectively (Table 4).

Two way ANOVA indicated a non significant observation between seasons (F =

5.49, p < 0.05) and stations ( F = 1.08, p < 0.05)during the year 2008 (Table 32).

3.3.15. Sulphate

Monthly variation of sulphate content of Thambraparani river in the year 2007 is given in

Fig. 32. The monthly concentration varied from 1 mg/L to 74mg/L in all the stations. The annual

mean values in the sampling stations occur in the order as S8>S5> S6>S7>S2>S4>S3> S1

(Table 1).

The recorded seasonal mean values are shown in Table 3. The values were found high

during monsoon (63.25) and lowest (0.50) during post-monsoon season in station 8 and 7

respectively.

Statistical analysis of two way ANOVA indicated that the variation of sulphate

concentration was influenced significantly by the seasons and stations (Table 33). Correlation

co-efficient showed a positive significant observation with EC, chloride, fluoride, phosphate,

alkalinity, magnesium, calcium, Hardness, turbidity, and DO and it was a negative significant

result with nitrate (r = - 0.512) (Table 37).

Monthly variation of sulphate content from Thambraparani river in the year 2008 is given

in Fig. 33. The monthly concentration varied from 1 mg/L to 59mg/L. The annual mean

concentrations of sulphate (mean ± S.D.) of different stations are shown in (Table 2). High mean

value of 41.41 ± 20.87 and low mean value of 5.75 ± 4.90 was reported at stations 8 and 1

respectively.

Seasonal mean values recorded on sulphate concentration was high during monsoon at

station 8 (53.25) and low (1.59) during post-monsoon season at station 5 (Table 4).

Statistical analysis of two way ANOVA indicated a significant influence of sulphate

by the seasons ( F = 13.86, p > 0.05) and stations ( F = 21.04 p > 0.05) (Table 34). Correlation

co-efficient showed a positive significant relationship with iron (r = 0.580; p > 0.05), turbidity (r

= 0.567; p > 0.05) and it was negative significant with nitrate (r = - 0.631; p < 0.05) and BOD (r

= - 0.575; p < 0.05) (Table 38).

3.3.16. Iron

The variation of iron concentration recorded in the Thambraparani river during the year

2007 is given in Fig.34. It was varied from 0 mg/L (October and November) to 0.35 mg/L (June)

at S1, from 0.3 mg/L (November and December) to 0.82 mg/L (June) at S2, from 0 mg/L

(October) to 0.2 mg/L (June) at S3, from 0.01 mg/L (October and November) to 0.28 mg/L

(July) at S4, from 0.03mg/L (October and November) to 2.95mg/L (September) at S5, from 0.2

mg/L (October) to 2.56 mg/L (September) at S6, from 0mg/L (October, November and

December) to 3.62 mg/L (July) at S7 and 0.01 mg/L (December) to 3.52mg/L (June) at S8. The

annual mean values recorded from different stations are shown in Table 1. It was low (0.14

mg/L) at station 8 and high (1.59 ± 1.28) at station 7.

The seasonal variations of mean values are shown in Table 4. and it was higher during

monsoon (2.98) and lower (0.002) during post-monsoon season in the station 7.

Two way ANOVA (Table 35) indicated a significant influence of iron content by the

seasons (F=9.87, p >0.05) and stations (F=21.71, p > 0.05). Correlation co-efficient showed a

positive significant observation with phosphate (r = 0.506; p > 0.05) alone (Table 37).

In the following year 2008, during November, December and January iron level was least

in all the sampling stations. In January, the lowest values were reported in S3 (0.03 mg/L), S4

(0.04 mg/L), S5 (0.09 mg/L), S6 (0.12 mg/L) and S7 (0.01 mg/L). In stations S2 (0.18 mg/L) and

S8 (0.06 mg/L), the least values were noticed in December. The peak value of iron content

ranged from 0.25 mg/L- 3.59 mg/L (Fig. 35). The annual mean values in the sampling stations

occur in the descending order as S7> S3> S6> S5> S2> S1> S4> S8 (Table 2).

Observation on seasonal studies resulted higher values during monsoon at station 7 (2.98)

and lower values (0.07) during post-monsoon season at station 7 and 4 respectively (Table 4).

Statistical analysis of two way ANOVA indicated that the variations of iron content was

strongly influenced by the seasons (F=7.68, p >0.05) and stations (F=14.53, p > 0.05) in the year

2008 (Table 36). Correlation co-efficient showed a positive significant relationship with

phosphate (r = 0.520; p > 0.05) and with turbidity (r = 0.663; p > 0.05). A negative significant

correlation was reported with nitrate (r = - 0.624; p < 0.05 (Table 38).

3. 4. Discussion

The river Thambraparani started its origin as an unpolluted river and the physic -chemical

parameters had a direct effect on water quality and influencing the biological activities prevailing

in water. In the current study, an assessment is made on baseline data in the water quality from

eight selected stations.

Temperature a catalyst, depressant an activator, a restrictor, a controller, a killer is one of

the most influential water quality characteristic of life in water. It is an important ecological

factor which determines the seasonal successions and distribution of flora and fauna in the

aquatic ecosystem (Das, 2000). In the present study, it was observed that, the surface water

temperature varied from 17ºC to 31ºC in the experimental stations. Lower temperature

observed in the month of June was due to the cloudy sky and heavy rainfall which brought down

the temperature to the minimum level (Kannan and Kannan, 1996). Such variation in

temperature was observed by Gothandaraman (1993); Karuppasamy (1997) and Seenivasan

(1998) from various water resources. In the summer months, the temperature remained high

because of the bright and long duration of solar radiation, low water levels and consequent high

atmospheric temperature similar to the present report in the river Palar at Vellore district the

maximum temperature reported was 31ºC during April (Govindswamy et al., 2007). In the

Vamanapuram river at South Kerala the reported temperature was from 23ºC to 33ºC

(Mayasubrahmani, 2007). In the Noon river at Uttar Pradesh, the maximum temperature

observed was from the minimum of 20ºC to the maximum of 31 ºC (Gupta et al., 2007). The

minimum temperature of Bennithora river at Karnataka was 19 ºC (June) to the maximum of

27.3 ºC (May) (Vijayakumar, 2004). In the Bhadra river, maximum temperature reported

was during summer seasons (Kiran et al., 2006). The lowest temperature observed was

19ºC in the river Tunga, during December month (Kumar et al., 2006 ) and in Narmada

river at Jabalpur (Sharma and Khokale, 1999 and Jain et al., 1996). Sheeba (1999) reported

temperature fluctuations in the Ithikara river. In the present study, the temperature fluctuation in

various stations depend upon geographical location, time of collection, seasons and also by the

mixing of effluents in the river system.

The pH of water is a precious indication of its quality and provides an important piece of

information in many types of geochemical equilibrium or solubility calculations (Hem, 1985). It

is also, one of the most single factors which influences aquatic production. The maximum

pH reported in the present study was 8.4 (station.8) and this result is inconsistency

with the observations of Pandey et al. (2004) in the Ramjan river system at Bihar and

Kadamuratti river at Tiruchirapalli district (Ramani et al., 2006). In the river Cauvery at

Tiruchirapalli also the maximum pH reported was 8.0 (Abdul and Hussain, 2003). The

maximum range of pH was from 8 to 9 in the river, Bennithora at Karnataka (Vijayakumar,

2004). In the river Thamiraparani at Tirunelveli, the pH of water varied from 6.5 to 8.5 (Jayavel

et al., 2010). The water pH ranged from 6.22 (September) to 8.4 (March) during the present

study. It showed a gradual but significant increase during pre-monsoon and low during

post-monsoon season. Ananthan (1994) has stated that the higher value of pH during pre-

monsoon was due to the uptake of CO2 by photosynthesizing organisms. The low pH observed

during monsoon season may be due to the influence of freshwater influx, dilution of sea water,

low temperature and organic matter decomposition as suggested by Ganesan (1992) and

Soundarapandian et al., (2009). Similar observations were reported by Thangaraj (1985) and

Hemalatha (1996). The observed low pH does not cause any harmful effects but it may

lead to increased desorption of metal cations due to composition of H+ ions as suggested

by Boominathan and Khan (1994). The acidic pH observed in station 2 was influenced by the

mixing of rubber factory effluents. pH value below 5.0 considerably reduces the productivity of

aquatic ecosystem and adversely affect the fishery resources (Santhosh, 2002).

Turbidity is caused due to the wide variety of suspended solid organic compounds,

microorganisms, clay, slit and coarse dispersion to sewage (Agarwal, 1999). The discharge of

untreated sewage waste and domestic effluent adds in greater quantities which increases turbidity

(Moin, 2001). Since turbidity reduces light penetration and most of the phytoplankton

production is restricted only in the superficial layer of water. The total turbidity values of

different stations varied from a minimum of 2 to 28 NTU during the entire study period.

Lower values were recorded during pre-monsoon season and these observations are similar to

those of Chetana et al., (1997) who have observed elevated turbidity during pre-monsoon period

in the river Cauvery at Karanataka, Singh and Singh (1995) and Gupta and Singh (2000) in the

river Damodar. During the study period, a decreasing trend in the turbidity was noticed

seasonally from monsoon to post-monsoon and to pre-monsoon and this was earlier reported by

Jerald, (1994) in a study on Anicut reservoir of Cauvery. In the present study, turbidity values

obtained were high during monsoon. Tessy (2010) has also reported high (0.6-13 NTU) turbidity

values during monsoon period and, it was due to the mixing of industrial waste, continuous sand

mining, agricultural waste etc. The high amount of solids recorded in the study sites, could be

attributed due to the different discharges as evidenced by Ushamary et al. (1998) in the river

Paravanar. The permissible limit of turbidity (BIS, 1991) of turbidity value is 2.5 NTU.

Electrical conductivity is another key factor that determines the quality of water which is

a measure of the ability of an aqueous solution to carry an electric current. This ability depends

on the presence of ions in their total concentration, mobility, and valence; and on the temperature

of measurement (Venkatesharaju et al., 2010). In the present study station 8 (Estuary) showed

extremely higher value (1190mMohs/cm) of Electrical conductance in contrast to other sites.

This finding coincides with the reports of Sheeja et al., (2008) who have studied the physico -

chemical properties of the river Thamiraparani (West). Increasing levels of conductivity and

cation are the products of decomposition and mineralization of organic materials (Abida and

Harikrishna, 2008). The electrical conductivity value was minimum (40 mMhos/cm) at station

1.The study also showed the presence of higher concentration of dissolved solids in the

form of inorganic salts in all the stations. The electrical conductivity reached its maximum

mean value mostly during monsoon and, this may be due to the natural concentration of ionized

substances present in the water and also by the higher dissolved solids (Ramdevi et al., 2009). In

the present study, minimum mean values were observed during post monsoon season and this

observation was in consistency with the earlier reports of Pandey (2001) in Ramaganga

river.

Hardness of water is an important consideration in determining the suitability of water for

domestic and industrial uses. It is mainly caused by multivalent metallic cations (Paul and

Mishra, 2004; Jayavel et al., 2009). High concentration of hardness may cause heart and kidney

problems (Sastry and Rathee, 1998; Pati et al., 2001; Freda et al., 2003). The prescribed limit

of hardness according to WHO is 500 mg/L. The principal hardness-causing cations are the

divalent calcium, magnesium and ferrous ions (Hem, 1991; Zhang et al., 1995; Gray, 2003;

Satyanarayana and Periakali 2003). The total hardness of the water samples reported from

different stations were shown in fig. 12 and 13 and in the present study it was very low (7

mg/L) during post-monsoon in station 1 and high in station 8 during monsoon (160 mg/L).

These observations are in agreement with those obtained by Bahadur and Chandra (1996); Pande

and Sharma (1998) and Ashish and Yogendra (2009) who have studied the water quality of

various river systems of India. In the river Ganga at Kanpur, Pratibha and Naheed (2006)

reported maximum hardness value of 167 mg/L. Water having hardness value more than

300 mg/L is undesirable for dyeing and textile industries and also for high temperature

boilers (Manivasakam, 1980).

The minimum level of total alkalinity needed for the water to be most productive

is > 50 ppm (Ohle, 1993). Alkalinity may be due to the minerals which dissolve in water

from soil. Alkalinity in itself is not harmful to human beings (Pande and Sharma,1999).

According to Kaur et al., (1996) high alkalinity values indicated the eutrophic nature of

the water body. It is also an important parameter used in corrosion control and helps in

evaluating the buffering capacity of waters (Bhaskar et al., 2005). Alkalinity of the water

can be defined as the capacity to neutralize a strong acid and is characterized by the

presence of all the hydrogen ions (Grag et al., 2008). The WHO limit of alkalinity is 200

mg/L. Increased concentration of alkalinity was attributed to the increased rate of

organic decomposition during which carbon-dioxide is liberated, that result with water to

form HCO3, thereby increasing alkalinity (Lewis, 1987 and Dudgeon, 1999). Although

alkalinity has little public health significance, highly alkaline waters are unpalatable and

are not used for domestic water supply (Patil et al., 2003). In the present study, maximum

alkalinity was recorded during the pre-monsoon and post-monsoon periods in all the 8 stations

and, the alkalinity value reached maximum in the month of August at S8 (90mg / l) and

minimum (4 mg/L) at station 2. The higher value of alkalinity is due to the presence of

bicarbonates and carbonates. Evidences supporting the above observations are reported in river

Jhelum exhibiting large quantities of bicarbonates during summer months which are ascribable to

the presence of excess of free carbon dioxide produced in the process of decomposition of

bottom deposits which probably resulted in conversion of insoluble carbonates into soluble

bicarbonates (Shyamsunder, 1988; Rincy and Tessy, 2010). Ruttner (1953) has also recorded

similar relationship. Adebsi (1980) showed alkalinity to be inversely related to the water.

Pandey and Cartney (1989) have published the same findings. In the present observation

alkalinity values are influenced significantly by different seasons according to statistical

analysis.

Among the physico-chemical parameters dissolved oxygen is very important for

the existence of plants and animals in the aquatic environment and determines water

quality (Reetakumari and Rani, 2008). Its concentration in a water body indicate its ability

to support aquatic life and reflect its physical and biological process prevailing in water.

It also affect the solubility and availability of many nutrients and therefore the

productivity of the aquatic system will also increase . Deficiency of dissolved oxygen directly

affects the ecosystem of a river due to bioaccumulation and biomagnifications. Dissolved oxygen

values also show lateral spatial and seasonal changes depending on industrial, human and

thermal activity (APHA, 1985). Low dissolved oxygen concentrations (< 3 mg/L) in freshwater

aquatics systems indicate high pollution level of the waters and cause negative effects on life in

this system (Yayıntas et al., 2007). Hacioglu and Dulger (2009) have reported a range of

dissolved oxygen concentration between 4.95 mg/L to 11.71 mg/L values, in the river Biga. The

oxygen concentration was high and significant in the surface waters of river Arkavathy (9.57)

mg/L, river Cauvery (10.0), Nambal river (8.7), Chalakudy river (8.0) and Gomti river (7.8). The

present study shows a high level of dissolved oxygen in pre - monsoon season and decreases

from monsoon to post-monsoon. This output is parallel to the observations of Thitame and

Pondhe (2010). In the present study the monthly observations of dissolved oxygen content

ranged from 3.51mg/L (March) in the eighth station to the maximum of 13.28 mg/L in

the fifth station during June 2007. In the river Jhelum maximum oxygen content was

reported during monsoon months (Raina et al., 1984 and Bhaskar et al., 2005). The low

values during non monsoon periods may be due to the increase in temperature of water

and active utilization of dissolved oxygen in bacterial decomposition of organic matter

and decay of vegetation (Singh and Srivastava, 1991 and Madahappan, 1993). Moreover

the low concentrations of oxygen at station 8 may be attributed to the retting activity prevalent in

these areas. Studies conducted by Bijoy and Nandan (1997) have clearly documented that the

depletion of dissolved oxygen was an outstanding feature in the retting zone. Higher

concentration of dissolved oxygen level during monsoon season of the present study is in

line with the earlier reports in the water quality assessment of river Yamuna by Susan et

al., (2005) and in Bhadra river by Kiran et al., (2006). Biological oxygen demand gives

an idea of quantity of degradable organic substances present in water which is subjected

to aerobic decomposition of microorganism. Thus it provides a direct measurement of

state of pollution (Sladeck et al., 1982 and Matur 2005). In the present study low

concentration (0.07 mg/L) of biological oxygen demand level was reported in the

month of July and the level reached maximum of 3.93 mg/L during pre-monsoon months.

The lower values reported were due to low biological activity and also by the mixing of

rain waters into the riverine system as reported by Tiwary et al., (2005) in the river

Ganga. It was supported by Martin and Haniffa (2003) that, the high biological oxygen

demand level of Tamiraparani river was due to the mixing of industrial effluent. Similar

trend was also noticed by Raina et al., (1984) and Pophali et al., (1990). In summer months,

high values of biological oxygen demand levels may be due to high temperature of the

water, decayed matters mixing in the river, decreased water level which stimulates the growth

of microorganisms. This often results in significant greater biological oxygen demand. But in

several systems, the biological oxygen demand level remains high. In rivers Kapila (2.97),

Cauvery (4.0) Suvarnavathy (3.56) and Simsha (3.72) it was significantly different. Similar

results were pointed out by Manimegalai et al., (1987) in Bhavani river. Tiwari et al., (1986)

and Sinha (1988) also reported high biological oxygen demand levels in other Indian

rivers. In the first station, the annual mean value of biological oxygen demand remains higher

due to the growth of plant community together with the phytoplankton population which may be

responsible for the depletion of oxygen as evidenced by Wong et al., (1978).

Phosphate (PO43-

) is present in natural waters as soluble phosphates and organic

phosphates. Agricultural runoff, fertilizers as well as waste water with detergents contribute the

main source. A large amount of chemical fertilizers, pesticides and herbicides used on crops,

and the organo phosphate insecticides used on grapes, pomegranates and other vegetables in the

field may get washed by rain water and reached the river through runoff (Sanjeevan and Sanjay,

2009). Detergents are the important contributor of phosphate (Bhaskar et al., 2005). Huge

quantities of detergents were used every day by the activities of the people in the river system.

Most of the waste water into the river systems from washing, laundries, factories, and other

industrial establishments also helps to increase the phosphate concentrations (Ananthraj et al.,

1987). Even then the phosphate concentration in the study area remained lower in all the

stations. It was ranged from a minimum of 0.01 mg/L (December) to a maximum of 0.65 mg/L

(June). High values during rainy season may be due to the rain water draining into the river with

nutrient rich soil deposited from the catchment areas and also by anthropogenic activities from

the nearby villages. This supports the observation of Ray and Banerjee (1978); Anter and

Danson (1993); Gupta and Pankaj (2006). The low values of the nutrients during winter season

might be due to its rapid utilization by the aquatic plants which conform the findings of Chole

(1989) The minimum amount of phosphate observed in the present study is in consistence

with earlier reports (Karthikeyani et al., 2002; Mathur and Maheswari, 2005).

Nitrate is a common form of combined nitrogen found in natural water. It is reduced to

nitrite by denitrification process under anaerobic conditions. Nitrate present in the aquatic

ecosystem will either be assimilated by algae and aquatic macrophytes or transferred to

underlying sediments (Drusila et al., 2005).The nitrate concentration in the present study

ranged from a minimum of 0.1 mg/L to 62 mg/L. In the experimental stations, it was

reported that concentration above 20 mg/L may cause methenoglobanemia a blood disease

in infants (Sunder and Mohanraj, 2008; Knobeloch, 2000 and Avery, 2001). But in the present

study, the concentration of nitrate is well within the limits in all the stations except the

estuarine station (S8) supporting the portability of water and the concentration of nitrogen

value remained higher during post-monsoon season. Maximum nitrate content of the river

water during pre-monsoon and post-monsoon period ranged between 1 to 62 mg/L. The above

observation agrees with that of Mangayarkarasi (1996) who has observed higher values during

the post-monsoon period. Similar results were reported by Shah (1988) and Kumar (1997). The

mean value of nitrate concentration was similar and less in the surface water of Cauvery (0.19

mg/L), river Kapila (0.21 mg/L) and river Shimsha (0.17 mg/L). But nitrate was high in

Suvarnavathi river (0.3 mg/L). This trend is due to the excess decomposition activity (Desai,

1995; Mahopatro and Pandey, 2001), and agricultural runoff (Kanhere and Gandale, 1999) in the

river. Like other nutrients, nitrate is also received by water bodies from drainage systems from

the surrounding areas and also by rainfall which has been described as an important source of

nitrate in freshwater bodies (Mangayarkarasi, 1996 and Jerald, 1994). The higher values

recorded during the post-monsoon period can be attributed to rain showers, decomposition of

organic matter, influx of flood water, addition of these nutrients from the agricultural runoff and

human settlements at various places. This is in accordance with the findings of Ray et al., (1966)

and the present result is also in good agreement with the observations of Jerald (1994) who has

reported that, nitrate content was higher during pre-monsoon than that of monsoon season.

Moreover, the effluent mixing may be the reason for increasing the maximum level of nitrogen

sources in the station 6 of the present study and it was evidenced by Manturawat (2006).

Chlorides occur naturally in all type of waters. It is one of the major inorganic anions in

river systems. In potable water the salty taste is produced by chloride concentration and it

depends on the chemical composition of the water (Mini et al., 2003). High concentration of

chlorides is considered to be the indicators of pollution due to organic wastes of animal or

industrial origin. Chlorides are trouble makers in irrigation water and also harmful to aquatic life

(Rajkumar, 2004). According to WHO, the higher concentration of chloride in drinking water

cause heart disease (Brooker and Johnson, 1984,) and hyper tension (Hussan and Ikbal, 2003).

The levels of chloride in the present investigation reached maximum (29.65 mg/L) in summer

and minimum (3 mg/L) in winter. In both the years of observation station 8 was reported with

high content of chloride which may be due to the human activities. This might be due to the less

quantity of waters, dumping of sewage, domestic waste and the release of human excreta (Goel,

1996). The present observations coincide with the findings of Thitame and Pondhe (2010) who

worked on physico-chemical properties of river, Pravara at Maharashtra. Sheeja et al., (2008) has

also observed the same in her studies. In the river system Gadana and Rama, the chloride

content reported was maximum of 92.0 mg/L (Ganesh et al., 2002). In river Thamiraparani

(west) the higher concentration was caused by dumping off sewage into the river system

(Susan et al., 2005; Sinha and Saxena, 2005).

Calcium is one of the most abundant elements in river water imparting hardness and most

of the geological materials of aquifers will be composed of calcium (Ajmal and Din, 1988;

Kamaraj et al., 2008). Calcium occurs due to the presence of limestone, gypsum etc. Calcium

concentration is contributed mainly by metamorphic reworked sediments mixed in the river

(Dhar et al., 1996) The high concentration of calcium in water is undesirable for washing and

bathing. The permissible limits of calcium in the drinking water is 100 ml/l and if present in

excess, it causes hypocalcaemia, coma and death (Dasgupta and Purphit 2001). In the present

study low values were reported in the first, second and fourth station (1 mg/L to2 mg/L) and

reached maximum in the eighth station (36 and 40 mg/L) during 2007 and 2008. The prescribed

amount of calcium, according to W H O is 250 mg/L. The present observations are in

agreement with previous workers, in the river Tunga (Kumar et al., 2006). Bordh et al.,

(2001) has reported 18 to 108 mg/L of calcium in the Bharalu river. The high values may be due

to the discharge of sewage. The low value shows the unpolluted nature of water. The same

reports were made by Kanakappan (2007) in the river Pazhayar.

Magnesium is a common constituent of natural water. Magnesium has a laxative and

diuretic effect (Grag et al., 2008).Geologically magnesium rich minerals will be associated with

basic and ultra basic rocks. According to W H O (1993), the maximum permissible limit of

magnesium is 150 mg/L. In the river Thambraparani the values ranged from 0 to 38 mg/L in the

years 2007 and 2008. The fluctuation in magnesium may be due to the increasing level of rain

fall and other sewage entry into the river. This observation is in conformity with the findings of

Adak et al., (2001) and Kumar (2006). Its high concentration reduces the utility of water for

domestic use and unpleasant taste to water (Drusila et al., 2005; Gray and Singh 2008).

Fluoride concentration is an important aspect of hydro geochemistry (Kamaraj et al.,

2008), because of its impact on human health. Dental and skeletal fluorosis is one of the serious

health problems of India (Park, 1997; Rao et al., 2004; Gupta et al., 2009; Dhakyanaika and

Kumara, 2010). The recommended concentration of F– in drinking water is 1.50 mg/L. Low

fluride content (< 0.60 mg/L) causes dental caries, whereas high (>1.20 mg/L) fluoride levels

result in fluorosis. Hence, it is essential to have a safe limit of its concentration of between 0.60

and 1.20 mg/L in drinking water (Venkatesharaju et al., 2010). The Bureau of Indian Standards

(BIS, 1991) prescribed a limit between 1.0 and 1.5 mg/L. In the present study the fluoride

content ranged from 0 mg/L to 0.9 mg/L, and it was well within the permissible limit (1.6mg/L).

Fluoride concentration does not show any significant seasonal and annual trends in their values

and, this result coincides with the result of Rao and Devadas (2003).

Sulphate is a naturally occurring anionic nutrient found almost in all aquatic ecosystems,

which may undergo transformation to sulphur or hydrogen suphide (Berner and Berner, 1987;

Drusila et al., 2005). Although, the maximum sulphate content was detected during monsoon

period (74mg/L) of station 8, during the study which may be due to the mixing of decomposed

organic substances, but it was within the prescribed limits. The reason for low content of

sulphate recorded in the present study was due to the absence of strong industrial pollution in the

sampling stations. The same trend was observed in Alagananda and Bhagirathi river

(Chakrapani, 2006). Some authors have suggested that the higher sulphate content in the rivers

and the flushing of these ions into the river from surface runoff during spring rains and decreases

during dry periods. (Qadri et al., 1981; ShyamSunder, 1988; Umamaheswari and Saravanan,

2009).

Iron is considered to be the most essential element of all organisms. It is present in

haemoglobin and myoglobin systems. Presence of iron content makes the water turbid,

discolored and imparts an astringent taste when water contains iron concentration above the

permissible limit (1 mg/L) in drinking water (Aowal, 1981; Shahnawaz and Singh, 2009). From

the data on the seasonal distribution of iron, it is clear that the iron content in the surface water of

rivers substantially increased during monsoon season and lowest levels observed during post-

monsoon season, the values were from 0 mg/L to 3.59 mg/L. This result agreed with the findings

of Sharma and Pandey (1999) Madhyastha (1996) and Shaikh (2009), in the rivers Ramaganga,

Nethravathi and Godawari. Kaushik et al., (2001) found the concentrations of 0.07 mg/L to

0.94mg/L in upper region of Yamuna river. Tiwari et al. (2005) analysed the water quality of

Ganga river in Bihar, and reported the iron content was 0.21 mg/ L during summer and 0.49

mg/L during rainy season. The increased level of iron content in the present study may be due to

the mixing of industrial waste, waste dumping by the village people into the river system.