Advertisement

Applied Water Science

, Volume 7, Issue 6, pp 3125–3135 | Cite as

Water quality assessment in terms of water quality index (WQI): case study of the Kolong River, Assam, India

  • Minakshi Bora
  • Dulal C. Goswami
Open Access
Original Article

Abstract

The Kolong River of Nagaon district, Assam has been facing serious degradation leading to its current moribund condition due to a drastic human intervention in the form of an embankment put across it near its take-off point from the Brahmaputra River in the year 1964. The blockage of the river flow was adopted as a flood control measure to protect its riparian areas, especially the Nagaon town, from flood hazard. The river, once a blooming distributary of the mighty Brahmaputra, had high navigability and rich riparian biodiversity with a well established agriculturally productive watershed. However, the present status of Kolong River is highly wretched as a consequence of the post-dam effects thus leaving it as stagnant pools of polluted water with negligible socio-economic and ecological value. The Central Pollution Control Board, in one of its report has placed the Kolong River among 275 most polluted rivers of India. Thus, this study is conducted to analyze the seasonal water quality status of the Kolong River in terms of water quality index (WQI). The WQI scores shows very poor to unsuitable quality of water samples in almost all the seven sampling sites along the Kolong River. The water quality is found to be most deteriorated during monsoon season with an average WQI value of 122.47 as compared to pre-monsoon and post-monsoon season having average WQI value of 85.73 and 80.75, respectively. Out of the seven sampling sites, Hatimura site (S1) and Nagaon Town site (S4) are observed to be the most polluted sites.

Keywords

Kolong River Embankment Post-dam effects Pollution Water quality index (WQI) 

Introduction

Freshwater sources in the form of rivers are very much essential for the sustenance and well being of a hale and hearty society. Unfortunately, during the last few decades these natural resources are continuously being tainted all around the world for the sake of development and flood hazard mitigation. However, north-east India is blessed enough to have bounty of accessible freshwater sources in the form of various rivers, streams, lakes, swamps, marshes, etc., with the mighty Brahmaputra river along with its numerous tributaries bifurcating the whole area. These rivers are the lifelines of these regions acting like arteries in our body and are supporting the social, ecological, cultural and overall environmental setup. Additionally, these rivers along with their numerous wetlands formed and feed by them also serve as the refuge to diverse organisms and sub-ecosystems.

Natural flow patterns are the heartbeat of a river. Each component of a flow regime—ranging from low flow to seasonal floods play a vital role in shaping a river ecosystem and livelihoods of river-dependent communities. Until recently, rivers of north-eastern region of India were in pristine free-flowing and unpolluted condition. However, during the last few decades in the pursuit to cope up with rest of the world in terms of development, our freshwater resources are continuously being tainted and deteriorated to an inconceivable stage. Out of various negative anthropogenic acts being perpetuated over our rivers those requiring special mention are water pollution from various point and non-point sources, damming (both for hydroelectricity generation as well as flood control), over abstraction and human encroachment. Ecosystems and communities dependent on natural flow regime have already experienced the adverse impacts of altered flow regimes due to engineering interventions. In nutshell, dams/embankments have regulated and fragmented the flows of our rivers—often irreplaceably and as a result, our rivers are inching towards their ecological and hydrological death.

Kolong River of Nagaon district in Assam is an appropriate example of such human intervention which is facing the gripe for the past fifty years. The Kolong River which once used to be a prize possession for the people of the state in general and for the people of Nagaon in particular, is presently gasping on its death-bed because of the ruthless and untenable act perpetrated on it in the name of engineering solution to the increasing flood hazard attributed to it in the aftermath of the great Assam earthquake of 1950.

During the years preceding 1964, primarily as a consequence of the great Assam earthquake of 1950 (measuring 8.7 on Richter scale), this region experienced repetition of large floods due mainly to raised bed level of the Brahmaputra through massive aggradation vis-à-vis the bed level of Kolong, leading thereby to its higher flood levels inundating adjoining low-lying areas like Nagaon. Mainly as a response to the increasing food hazard faced by the district administrative headquarter, i.e., the Nagaon town, an ad hoc flood control measure was undertaken by constructing an earthen embankment, known as Hatimura dyke, across the river’s take-off point near Hatimura in the year 1964. This drastic human intervention has end up in converting the once free flowing river into a string of alternating dry stretches and stagnant pools during the decades that followed (Bora and Goswami 2014). The river in the present scenario with negligible self-purification capacity is facing severe anthropogenic pressure and acts as the receiver of huge amount of point and non-point pollutants. Consequently, the Kolong River is listed among the 275 most polluted rivers of India by the Central Pollution Control Board (CPCB 2015). Furthermore, drastic changes in landuse/landcover (LULC) pattern of the Kolong River basin have been reported by Bora and Goswami (2016). To restore the health of the Kolong River, a sustainable river-restoration plan seeks its exigency. Thus, the overall aim of the present investigation is to finalize the prevailing water quality inventory of the Kolong River based on WQI and then to propose effective measures to revitalize the Kolong River within the milieu of the continued urbanization by restoring it to its natural state, while allowing the river system to continue to support flood management, landscape development and recreational activities.

A water quality index (WQI) helps in understanding the general water quality status of a water source and hence it has been applied for both surface and ground water quality assessment all around the world since the last few decades (Samantray et al. 2009; Sharma and Kansal 2011; Alam and Pathak 2010; Sebastian and Yamakanamardi 2013; Seth et al. 2014; Tyagi et al. 2013; Bhutiani et al. 2014; VishnuRadhan et al. 2015; Yadav et al. 2015; Dash et al. 2015; Krishnan et al. 2016; Kaviarasan et al. 2016). The main purpose of developing a WQI is to transform a complex set of water quality data into lucid and exploitable information by which a layman can know the status of the water source (Akoteyon et al. 2011; Balan et al. 2012). WQI aims at giving a single value to the water quality of a source by translating the list of parameters and their concentrations present in a sample into a single value, which in turn provides an extensive interpretation of the quality of water and its suitability for various purposes like drinking, irrigation, fishing etc. (Abbasi 2002).

Although, water pollution is a chief matter of apprehension in regard to Kolong River, the water quality issue of the river has not yet got its due importance. However, few scientific investigations on water quality assessment of Kolong River (Saikia and Sarma 2011; Barbaruah et al. 2012; Khan and Hazarika 2012; Bora and Goswami 2014, 2015) have been reported. Fluoride geochemistry of Kolong River was discussed elaborately by Saikia and Sarma (2011). They found that the fluoride concentration of groundwater samples collected from Kolong River basin ranged between 0.03 and 5.68 mg/l. Khan and Hazarika (2012) reported that the increased pollution level of Kolong River water is mainly attributed by the discharge of various types of domestic and commercial waste water, sewage and effluent. Moreover, the truncated river flow accompanied with diminished flow velocity has reduced the self-assimilation and self-purification capacity of the Kolong River (Bora and Goswami 2015). Ironically, literature survey revealed the fact that so far no detailed work on WQI has been carried out for Kolong River. Hence, in continuation of our previous work (Bora and Goswami 2014, 2015), the present investigation is carried out to establish the general pollution trend of the river and to determine the aptness of the water for various purposes based on a set of observed water quality parameters. In this context, an attempt has been made to determine the fitness of various water samples collected along Kolong River for different uses, using the ‘weighted arithmetic index method’ given by Brown et al. (1970).

Study area

This study is conducted in the Kolong River which is an important river of middle Assam. The Kolong River with a total length of about 212 km is a distributary (Suti in local language) of the Brahmaputra which branches out from the near Jakhalabandha, about 77 km upstream of Nagaon, and meets it again at Kajalimukh near Guwahati in a joint channel with the Kopili River—a major south bank tributary of Brahmaputra that flows into Kolong near Jagibhakatgaon of Morigaon district (Fig. 1). The river during its course traverses through the plains of Nagaon, Morigaon and Kamrup districts of Assam. During the course from source to mouth the Kolong River is joined by three main tributaries namely Misa, Dizu and Haria.
Fig. 1

Map showing the study area

Materials and methods

Water samples were collected from seven sampling sites viz. Hatimura (S1), Missamukh (S2), Dizumukh (S3), Nagaon town (S4), Hariamukh (S5), Jagibhakatgaon (S6) and Kajalimukh (S7) during pre-monsoon (PRM), monsoon (MON) and post-monsoon (POM) season over a period of three years, i.e., from January 2012 to November 2015. The details of sampling sites are shown in Fig. 2.
Fig. 2

Map showing sampling sites

Various physico-chemical parameters of the water samples were analyzed by following the standard methods of APHA (2005) and Trivedy and Goel (1986). A set of ten most commonly used water quality parameters namely pH, electrical conductivity (EC), total dissolved solid (TDS), total suspended solid (TSS), chloride, total alkalinity (TA), total hardness (TH), dissolved oxygen (DO), biochemical oxygen demand (BOD) and sulphate which, together, reflect the overall water quality of the Kolong River were selected for generating the water quality index (WQI). Calculation of WQI was carried out by following the ‘weighted arithmetic index method’ (Brown et al. 1970), using the equation:
$${\text{WQI}} = {{\sum Q_{n} W_{n} } \mathord{\left/ {\vphantom {{\sum Q_{n} W_{n} } {\sum W_{n} }}} \right. \kern-0pt} {\sum W_{n} }}$$
where Q n is the quality rating of nth water quality parameter, W n is the unit weight of nth water quality parameter.
The quality rating Q n is calculated using the equation
$$Q_{n} = 100\,[(V_{n} - V_{i} )/(V_{\text{s}} - V_{i} )]$$
where V n is the actual amount of nth parameter present, V i is the ideal value of the parameter [V i  = 0, except for pH (V i  = 7) and DO (V i  = 14.6 mg/l)], V s is the standard permissible value for the nth water quality parameter.
Unit weight (W n ) is calculated using the formula
$$W_{n} = k/V_{\text{s}}$$
where k is the constant of proportionality and it is calculated using the equation
$$k = \left[ {{1 \mathord{\left/ {\vphantom {1 {\sum 1/V_{\text{s}} = 1,\,2,\, \ldots ,\,n}}} \right. \kern-0pt} {\sum 1/V_{\text{s}} = 1,\,2,\, \ldots ,\,n}}} \right].$$
The water quality status (WQS) according to WQI is shown in Table 1.
Table 1

WQI range, status and possible usage of the water sample (Brown et al. 1972)

WQI

Water quality status (WQS)

Possible usage

0–25

Excellent

Drinking, irrigation and industrial

26–50

Good

Drinking, irrigation and industrial

51–75

Poor

Irrigation and industrial

76–100

Very poor

Irrigation

Above 100

Unsuitable for drinking and fish culture

Proper treatment required before use

Results and discussions

For calculating WQI, the prime pre-requisite is the results of various water quality analyses. The statistical summary of the selected water quality parameters at various sampling sites of the Kolong River during PRM, MON and POM season is presented in Table 2.
Table 2

Descriptive statistics for the water quality parameters of the Kolong River

Parameter

Pre-monsoon

Monsoon

Post-monsoon

pH

7.11 ± 0.52 (6.31–7.59)

6.65 ± 0.06 (6.59–6.75)

6.57 ± 0.34 (6.23–7.12)

EC (μS/cm)

1302.3 ± 340 (1017–1900)

170 ± 122 (60–410)

140 ± 50 (90–199)

TDS (mg/l)

313.55 ± 44.97 (250–370)

257.69 ± 32.9 (210.75–299)

153.28 ± 18.66 (122–175)

TSS (mg/l)

81.14 ± 13.7 (65–105)

144.05 ± 27.37 (97.88–178.21)

65.68 ± 16.04 (48–78)

TH (mg/l)

90.86 ± 41.03 (52–164)

140.71 ± 70.5 (88–288)

183.43 ± 87.62 (72–296)

Cl (mg/l)

69.12 ± 15.6 (45.44–94.56)

55.6 ± 8.6 (45.44–71)

25.52 ± 5.2 (19.88–34.08)

DO (mg l−1)

9.22 ± 4.9 (0–13.77)

2.96 ± 1.07 (0.81–4.05)

7.8 ± 3 (3.4–12.83)

BOD (mg/l)

8.19 ± 3.6 (4.2–13.3)

10.98 ± 3.9 (7.06–17.8)

7.96 ± 3.8 (4.3–15.01)

\({\text{SO}}_{2}^{ - 4}\) (mg/l)

12.6 ± 5.4 (6.64–21.64)

15.45 ± 4.9 (9.82–21.9)

13.27 ± 4.35 (7.07–20.74)

TA (mg/l)

210.7 ± 70.5 (125–300)

231.43 ± 96.5 (100–360)

154.14 ± 58.1 (100–255)

Values are expressed in mean ± SD (the values in parentheses denotes the range of each parameter)

pH generally signifies the degree of acidity or alkalinity of a water sample. The average pH values for PRM, MON and POM season were 7.11 ± 0.52, 6.65 ± 0.06 and 6.57 ± 0.34, respectively. Although the average pH values were within the BIS prescribed limits, however, the minimum pH values during PRM and POM were below the prescribed limit, i.e., 6.5–8.5. Electrical conductivity measures the electric current carrying capacity of a water sample and is directly related to the dissolved ions present in the water. EC was measured using a digital conductivity meter and the results were expressed in microsiemen/centimeter. Observed EC values for the water samples of the Kolong River ranged between 1017–1900 μS/cm (±340), 60–410 μS/cm (±122) and 90–199 μS/cm (±50) during PRM, MON and POM season, respectively, with the values exceeding the ICMR standard of 300 μS/cm at some of the sampling sites during PRM and MON seasons.

TSS and TDS are, respectively, the direct measurement of total suspended and dissolved particles present in a water sample and BIS desirable limit for both the parameters are 500 mg/l. Suspended and dissolved solids are both organic as well as inorganic in nature. The concentration of TSS for the water samples ranged from 65 to 107 mg/l (±13.7) during PRM, from 97.88 to 178.21 mg/l during MON and from 48 to 78 mg/l during POM season, which were well within the BIS desirable limit of 500 mg/l. Similarly, TDS values were also within the desirable limit with mean values of 313.55 mg/l (±44.97), 257.69 mg/l (±32.9) and 153.28 mg/l (±18.66) during PPM, MON and POM season, respectively.

Hardness implies the lather forming capacity of a water sample and the two cations mainly responsible for hardness of water are calcium and magnesium. The observed values of total hardness for the water samples of the Kolong River during PRM, MON and POM season ranged from 52 to 164 mg/l (±41.03), 88 to 288 mg/l (±70.05) and 72 to 296 mg/l (±87.62), respectively, and the values were within the desirable limit of 300 mg/l. Based on the hardness values, Kolong River water generally falls under moderately hard to hard water category.

Chloride is one of the important WQ parameter and is widely distributed in nature in the form of salts of sodium (NaCl), potassium (KCl) and calcium (CaCl2). Various sources contributing chloride in water are leaching from various rocks by the process of weathering, surface run-off from inorganic fertilizers dependent agricultural fields, irrigation discharge, animal feeds, etc. The average chloride concentration for the studied water samples during PRM, MON and POM season were 45.44 to 94.56 mg/l (±15.6), 45.44 to 71 mg/l (±8.6) and 19.88 to 34.08 mg/l (±5.2), respectively. The observed chloride concentrations were well within the desirable limit cited by BIS, i.e., 250 mg/l.

Amount of total oxygen dissolved in a water body is termed as dissolved oxygen (DO) and its concentration depend on physical, chemical and biological activities of the water body. Estimation of DO is very much essential in water pollution control. A DO level of 4–6 mg/l is optimum range for a good water quality sustaining aquatic life. Water sample with DO concentration below this optimum range is expected to be polluted. The mean DO values ranged from a minimum of 2.96 mg/l (±1.07) during MON season to a maximum of 9.22 mg/l (±4.9) during PRM season. DO is nil (0 mg/l) at site S1 during PRM, attributed chiefly by the high stagnancy of the water source due to lack of sufficient flow.

The total amount of oxygen required by aerobic micro-organisms for complete degradation of organic wastes present in a water body is termed as biochemical oxygen demand (BOD). Thus, BOD is an indicator of organic pollution with higher values indicating higher levels of organic pollution (Patel et al. 1983). BOD values above 5 mg/l are undesirable and the present analysis revealed the mean BOD values as 8.19 mg/l (±3.6), 10.98 mg/l (±3.9) and 7.96 mg/l (±3.8) during PRM, MON and POM season, respectively, with values exceeding the desirable limit. The higher values of BOD emphasized the presence of prominent organic pollution source near the sampling sites.

Occurrence of sulphate in river water is mainly natural in nature contributed chiefly by mineral sources like gypsum, etc. Although in small concentration sulphate is harmless, however, high concentration of sulphate in drinking water may cause various intestinal diseases. Mean sulphate concentration of the water samples under investigation varied from 12.6 mg/l (±5.4) during PRM season to 15.45 mg/l (±4.9) during MON season and the values were within the standard limit of 150 mg/l as per BIS.

Total alkalinity is the capability of an aqueous solution to neutralize an acid. Alkalinity is due to the various carbonate, bicarbonate and hydroxide ions present in water. The mean concentration of alkalinity in water samples was observed to be 210.7 mg/l (±70.5), 231.43 mg/l (±96.5) and 154.14 mg/l (±58.1) during PRM, MON and POM season, respectively. The mean alkalinity values exceeded the BIS prescribed limit of 120 mg/l during all the seasons.

WQI analysis

The first step in calculation of WQI following ‘weighted arithmetic index’ method involves the estimation of ‘unit weight’ assigned to each physico-chemical parameter considered for the calculation. By assigning unit-weights, all the concerned parameters of different units and dimensions are transformed to a common scale. Table 3 shows the drinking water quality standards and the unit-weights assigned to each parameter used for calculating the WQI. Maximum weight, i.e., 0.366 is assigned to both DO and BOD, thus suggesting the key significance of these two parameters in water quality assessment and their considerable impact on the index.
Table 3

Relative weights (W n ) of the parameters used for WQI determination

Parameter

ICMR/BIS standard (V s )

Unit weight (W n )

pH

6.5–8.5

0.215

Electrical conductivity

300

0.0061

TDS

500

0.00366

TSS

500

0.00366

Total hardness

300

0.0061

Chloride

250

0.00732

DO

5

0.366

BOD

5

0.366

Sulphate

150

0.0122

Total alkalinity

120

0.01525

\(\sum {}\) W n  = 1.001

All the parameters are in milligrams per liter except pH and EC (μS/cm)

The observed values of the selected physico-chemical parameters in all the sampling sites for each season and the corresponding WQI values are presented in tabular form (Tables 4, 5, 6, 7, 8, 9, 10). Out of the ten parameters considered for this study, DO and BOD were found to be the highest influencing parameters in the WQI scores (Tables 4, 5, 6, 7, 8, 9, 10).
Table 4

Calculation of WQI at site S1

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

6.5

−33.33

−6.13

6.64

−24

−4.416

6.23

−51.33

−11.04

EC

1690

563.33

3.43

411

137

0.83

199

66.33

0.404

TDS

250

50

0.1565

230.45

46.09

0.144

122

24.4

0.089

TSS

65

13

0.041

122.5

24.5

0.0767

60

12

0.044

TH

164

54.66

0.28

92

30.66

0.159

140

46.66

0.28

Chloride

76.86

30.74

0.19

53.96

21.58

0.135

34.08

13.632

0.1

DO

0

152.08

47.6

2.43

126.77

39.679

3.4

116.66

42.7

BOD

13.3

266

83.258

17.8

356

130.3

15.01

300.2

109.87

Sulphate

9.915

6.6

0.068

12.03

8.02

0.0834

11.6

7.73

0.1

TA

275

229.16

2.98

220

183.33

2.383

100

83.33

1.27

 

\(\sum {}\) W n Q n  = 131.87

\(\sum {}\) W n Q n  = 169.37

\(\sum {}\) W n Q n  = 143.82

WQI = 131.74

WQI = 169.2

WQI = 143.67

Table 5

Calculation of WQI at site S2

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

6.31

−46

−9.89

6.6

−26.66

−5.732

6.26

−49.33

−10.605

EC

1017

339

2.07

169.9

56.63

0.345

130

43.33

0.264

TDS

269

53.8

0.197

250.66

50.132

0.1834

150

30

0.1098

TSS

88

17.6

0.064

160.73

32.146

0.118

78

15.6

0.057

TH

52

17.33

0.1057

100

0.333

0.002

228

0.76

0.0046

Chloride

59.64

23.856

0.1746

53.96

0.2158

0.0016

19.88

7.952

0.0582

DO

5.16

98.3

35.98

3.24

118.33

43.31

6.1

88.54

32.405

BOD

8.4

168

61.488

12.58

251.6

92.08

9.8

196

71.736

Sulphate

12.84

8.56

0.1044

15.1

10.066

0.123

13

8.66

0.1056

TA

275

229.16

3.49

360

300

4.575

100

83.33

1.27

 

\(\sum {}\) W n Q n  = 93.78

\(\sum {}\) W n Q n  = 135.01

\(\sum {}\) W n Q n  = 95.4

WQI = 93.7

WQI = 134.87

WQI = 95.3

Table 6

Calculation of WQI at site S3

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

7.25

16.66

3.58

6.75

−16.66

−3.58

6.52

−32

−6.88

EC

1048

349.33

2.13

207

69

0.42

109.3

36.43

0.22

TDS

345

69

0.25

257.97

51.594

0.188

150

30

0.1098

TSS

72

14.4

0.0527

165

33

0.12

56

11.2

0.0409

TH

68

22.66

0.138

129

40

0.244

76

25.33

0.1545

Chloride

62.48

24.992

0.1829

62.48

24.99

0.1829

19.88

7.952

0.0582

DO

11.35

33.85

12.389

3.24

118.33

43.308

8.11

67.6

24.74

BOD

6.31

126.2

46.19

9.1

182

66.612

4.3

86

31.476

Sulphate

6.587

4.39

0.0535

9.82

6.546

0.0798

7.07

4.71

0.0574

TA

175

145.83

2.22

260

216.66

3.3

175

145.83

2.22

 

\(\sum {}\) W n Q n  = 67.2

\(\sum {}\) W n Q n  = 110.87

\(\sum {}\) W n Q n  = 52.2

WQI = 67.13

WQI = 110.76

WQI = 52.15

Table 7

Calculation of WQI at site S4

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

7.47

31.33

6.736

6.6

−26.66

−5.732

6.32

−45.33

−9.746

EC

1885

628.33

3.83

191

63.66

0.4

159

53

0.32

TDS

370

74

0.27

300

60

0.22

166

33.22

0.1216

TSS

80

16

0.058

143.87

28.77

0.1053

66.78

13.356

0.0488

TH

128

42.66

0.26

288

96

0.5856

296

98.66

0.602

Chloride

94.56

37.824

0.277

53.96

21.584

0.158

28.4

11.36

0.083

DO

10.54

42.29

15.48

0.81

143.64

52.57

6.08

88.75

32.48

BOD

12.9

258

94.43

13.88

277.6

101.6

9.7

194

71.004

Sulphate

6.64

4.42

0.054

11

7.33

0.0894

10.31

6.87

0.0838

TA

300

250

3.81

340

283.33

4.32

255

187.5

2.86

 

\(\sum {}\) W n Q n  = 125.2

\(\sum {}\) W n Q n  = 154.32

\(\sum {}\) W n Q n  = 97.86

WQI = 125.07

WQI = 154.16

WQI = 97.76

Table 8

Calculation of WQI at site S5

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

7.59

39.33

8.45

6.66

−22.66

−4.89

6.9

−6.66

−1.43

EC

1062

354

2.16

84.6

28.2

0.17

97

32.33

0.2

TDS

350

70

0.256

210.75

42.15

0.154

170

34

0.124

TSS

105

21

0.077

178.21

35.64

0.13

95

19

0.069

TH

88

29.33

0.18

168

56

0.34

240

80

0.488

Chloride

68.16

27.26

0.199

45.44

18.176

0.133

25.26

10.104

0.0739

DO

11.6

31.25

11.437

3.7

113.54

41.55

9.12

57.08

20.89

BOD

4.2

84

30.74

7.5

150

54.9

6.3

126

46.116

Sulphate

15.47

10.31

0.1257

16.4

10.93

0.133

14.4

9.6

0.117

TA

150

125

1.9

200

1.66

0.025

175

148.83

2.73

 

\(\sum {}\) W n Q n  = 55.52

\(\sum {}\) W n Q n  = 92.64

\(\sum {}\) W n Q n  = 69.4

WQI = 55.46

WQI = 92.55

WQI = 69.33

Table 9

Calculation of WQI at site S6

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

7.21

14

3.01

6.59

−27.33

−5.876

7.12

8

1.72

EC

1161

387

2.36

69

23

0.14

212.2

70.73

0.43

TDS

290

58

0.212

255

51

0.1866

175

35

0.128

TSS

70

14

0.0512

97.88

19.576

0.0716

48

9.6

0.035

TH

80

26.66

0.1626

120

40

0.244

232

77.33

0.472

Chloride

45.44

18.176

0.133

48.28

19.312

0.141

22.72

9.088

0.0665

DO

13.778

8.56

3.133

4.054

109.85

40.205

12.83

18.437

6.75

BOD

5.34

106.8

44.65

7.06

141.2

51.68

5.1

102

37.33

Sulphate

15.184

10.123

0.1235

21.9

14.6

0.178

15.8

10.53

0.128

TA

125

104.16

1.588

100

83.33

1.271

175

145.83

2.22

 

\(\sum {}\) W n Q n  = 55.42

\(\sum {}\) W n Q n  = 88.24

\(\sum {}\) W n Q n  = 49.3

WQI = 55.36

WQI = 88.15

WQI = 49.25

Table 10

Calculation of WQI at site S7

Parameter

PRM

MON

POM

V n

Q n

Q n W n

V n

Q n

Q n W n

V n

Q n

Q n W n

pH

7.44

29.33

6.306

6.7

−20

−4.3

6.63

24.66

−5.3

EC

1253

417.66

2.55

60

20

0.122

90

30

0.183

TDS

320.88

64.176

0.235

299

59.8

0.2188

140

28

0.102

TSS

88

17.6

0.064

140.18

28.036

0.1026

56

11.2

0.041

TH

56

18.66

0.1138

88

29.33

0.1789

72

24

0.146

Chloride

76.68

30.67

0.224

71

28.4

0.208

28.4

11.36

0.083

DO

12.16

25.42

9.3

3.24

118.33

43.31

9.12

57.08

20.89

BOD

6.9

138

50.51

9

180

65.88

5.5

110

40.26

Sulphate

21.637

14.42

0.176

21.9

14.6

0.178

20.74

13.83

0.168

TA

175

145.83

2.224

140

116.66

1.779

100

83.3

1.27

 

\(\sum {}\) W n Q n  = 71.7

\(\sum {}\) W n Q n  = 107.7

\(\sum {}\) W n Q n  = 57.84

WQI = 71.63

WQI = 107.59

WQI = 57.78

The summary of WQI values of the water samples from all the seven sampling sites for each season are presented in Table 11 given below. The results showed that majority of the water sample fall under very poor (75 < WQI < 100) and unsuitable water category (WQI > 100). Highest WQI values were recorded during monsoon season with values ranging from a low of 88.15 at site S6 to a high of 169.2 at site S1 with an average WQI value of 122.47 ± 30.02 (Table 11). The unsuitability of river water during monsoon season is mainly attributed by increased surface run-off from the adjacent urban agglomerations and direct discharge from storm water drains along roads adjacent to the river; similar results were also observed by Sebastian and Yamakanamardi (2013) in case of Cauvery River. The WQI analysis unveiled the fact that site S1 and site S4 were the two most polluted sites along the entire reach of the Kolong River. The WQI values of site S1 specified the fact that the water was unsuitable for any use including drinking, fish culture and irrigation during all the sampling season (Table 1). In addition to high domestic sewage disposal and eutrophication of the water body, the append reason behind high pollution level of site S1 is the lack of sufficient flow leading to the stagnancy of the water, which in turn reduced the self-assimilation capacity of the riverine ecosystem. Analogous effects of altered river flow on water quality were also reported in Tunga-Bhadra River by Rehana and Mujumdar (2011). Similarly, site S4, i.e., Nagaon town, the most populated urban agglomeration along Kolong River also witnessed a highly deteriorated water quality mainly contributed by huge demographic as well as socio-economic pressure in the form of river bed encroachment and river water exploitation for various chores. Thus, site S4 acquired very poor to unfit water quality status as indicated by the WQI values ranging between 97.76 during post-monsoon season to 154.16 during monsoon season (Table 11). Likewise, the fetid water quality at sites S2, S3, S5 and S7 is a result of the pollution contributed by the nearby urban settlements namely Missa town, Amoni, Raha and Chandrapur, respectively. The high WQI scores in all the above sites are contributed mainly by various anthropogenic activities like the inflow of direct sewerage from residential and commercial establishments, lack of proper sanitation system, agricultural run-off, direct disposal of untreated effluents from small scale industries and factories and unabated dumping of solid wastes by the communities residing alongside the river, etc. It is clear from Tables 4, 5, 6, 7, 8, 9 and 10 that BOD and DO were the two deciding parameters exhibiting the maximum influence in WQI calculation. The Kolong River water samples experienced lower DO concentration and higher BOD concentration, thus signifying high organic pollution load.
Table 11

Summary of WQI of the Kolong River

Sampling station

PRM

MON

POM

WQI

WQS

WQI

WQS

WQI

WQS

S1

131.74

Unsuitable

169.2

Unsuitable

143.67

Unsuitable

S2

93.7

Poor

134.87

Unsuitable

95.3

Very poor

S3

67.13

Poor

110.76

Unsuitable

52.15

Poor

S4

125.07

Unsuitable

154.16

Unsuitable

97.76

Very poor

S5

55.46

Poor

92.55

Very poor

69.33

Poor

S6

55.36

Poor

88.15

Very poor

49.25

Good

S7

71.63

Poor

107.59

Unsuitable

57.78

Poor

Average

85.73

 

122.47

 

80.75

 

The WQI values of site S6, i.e., near Jagibhakatgaon, a rural area, was comparatively better among all the studied sites with values ranging from 49.25 during post-monsoon season to 88.15 during monsoon season. The comparatively improved water quality condition at site S6 is mainly because of the dilution of the polluted Kolong River water with less polluted Kopili River water, besides the absence of any major urban agglomeration.

The pollution level as supported by the WQI value showed a mixed pattern of change during all the sampling seasons (Fig. 3). Figure 3 clearly indicates that while moving in the downstream direction, the pollution level gradually decreases from station S1 up to station S3. Whereas station S4 experiences an abrupt raise in pollution level, justified by the demographic as well as commercial pressure at the site. Further downstream, water samples showed a decreasing pollution trend up to site S6. Site S7 again rendered an increased pollution level when compared to its immediate upstream sampling site, i.e., site S6, mainly supported by the fact that site S7 is located near an urban agglomeration dominated by brick knils and a market place. While the fall in graph towards site S6 is supported by the fact that unlike other sampling locations the aforementioned sampling site is located near a rural settlement with no major source of water pollutants as discussed earlier.
Fig. 3

WQI rating of various sampling sites of Kolong River

In monsoon season, the water qualities of all the sampling sites were found unsuitable except at site S5 and S6 where the water is of very poor quality, as depicted in Fig. 3. During pre-monsoon season, water quality of the sampling sites was found to fall under unsuitable to poor water quality. During post-monsoon season, site S6 experienced marginally good water quality while the rest lied in unsuitable, very poor and poor water quality category (Fig. 3). Interestingly, the WQI scores for site S1 showed unsuitable water quality status during every sampling season mainly because of the lack of sufficient flow in addition to increased organic pollution load, thus reducing the self-purification capacity of the river at the site.

Conclusion

Water quality index is helpful in assessment and management of water quality. The present investigation represents the first of its type undertaken on the Kolong River of Assam. The case study provides valuable insight into the status of overall suitability of the Kolong River water based on WQI values. It highlights the salient features of various important physico-chemical parameters acting upon the general water quality of the river. The season wise variations in the WQI values were examined based on seasonal water quality analysis data of seven sampling sites distributed along the river channel. The baseline data generated in these investigations and their analysis and interpretation will go a long way in improving our understanding and knowledge base about the status of water quality of a socio-economically vital fluvial system, i.e., the Kolong River and the factors affecting the overall quality of its water. The study has both academic value and practical significance. Based on observed WQI results it can be concluded that effective treatment measures are urgently required to augment the river water quality by defining an appropriate water quality management plan which in turn will support any future plan for sustainable river restoration. Water quality of the river needs to be restored by adopting measures like restricting inflows of raw sewerage from residential/commercial establishments, limiting direct discharge from storm water drains into the river and preventing unabated dumping of solid waste by communities residing along the river. Besides, desilting measures to improve the carrying capacity of the river channel needs to be adopted and existing encroachments for settlement and infrastructural development should be removed.

Notes

Acknowledgments

The authors’ thanks are due to the INSPIRE program sponsored by the Department of Science and Technology (DST), Government of India for extending the essential financial assistance during the study.

References

  1. Abbasi SA (2002) Water quality indices. Elsevier, AmsterdamGoogle Scholar
  2. Akoteyon IS, Omotayo AO, Soladoye O, Olaoye HO (2011) Determination of water quality index and suitability of urban river for municipal water supply in Lagos, Nigeria. Eur J Sci Res 54(2):263–271Google Scholar
  3. Alam M, Pathak JK (2010) Rapid assessment of water quality index of Ramganga River, Western Uttar Pradesh (India) using a computer programme. Nat Sci 8(11):1–8Google Scholar
  4. APHA (2005) Standard methods for examination of water and wastewater, 21st edn. American Public Health Association, WashingtonGoogle Scholar
  5. Balan IN, Shivakumar M, Kumar PDM (2012) An assessment of groundwater quality using water quality index in Chennai, Tamil Nadu, India. Chron Young Sci 3(2):146–150CrossRefGoogle Scholar
  6. Barbaruah AD, Phukan SS, Dutta A (2012) A comparative study of impact of water and soil quality on fish diversity of Monoha beel and Elenga beel of Morigaon, India. Clar Int Multidiscip J 1(2):94–100Google Scholar
  7. Bhutiani R, Khanna DR, Kulkarni DB, Ruhela M (2014) Assessment of Ganga river ecosystem at Haridwar, Uttarakhand, India with reference to water quality indices. Appl Water Sci. doi:  10.1007/s13201-014-0206-6
  8. Bora M, Goswami DC (2014) Study for restoration using field survey and geoinformatics of the Kolong River, Assam, India. J Environ Res Dev 8(4):997–1004Google Scholar
  9. Bora M, Goswami DC (2015) A study on seasonal and temporal variation in physico-chemical and hydrological characteristics of river Kolong at Nagaon Town, Assam, India. Arch Appl Sci Res 7(5):110–117Google Scholar
  10. Bora M, Goswami DC (2016) Spatio-temporal landuse/landcover (LULC) change analysis of Kolong River basin, Assam, India using geospatial technologies. Int J Geomat Geosci 6(3):1676–1684Google Scholar
  11. Brown RM, McClellan NI, Deininger RA, Tozer RG (1970) A water quality index—do we dare? Water Sew Works 117:339–343Google Scholar
  12. Brown RM, McClelland NI, Deininger RA, O’Connor MF (1972) A water quality index—crashing the physiological barrier. Indic Environ Qual 1:173–182Google Scholar
  13. Central Pollution Control Board (2015) River stretches for restoration of water quality. A Ministry of Environment, Forest and Climate change reportGoogle Scholar
  14. Dash A, Das HK, Mishra B, Bhuyan NK (2015) Evaluation of water quality of local streams and Baitarani River in Joda area of Odisha, India. Int J Curr Res 7(3):13559–13568Google Scholar
  15. Kaviarasan M, Geetha P, Soman KP (2016) GIS-based groundwater monitoring in Thiruvannamalai District, Tamil Nadu, India. In: Proceedings of International Conference on Soft Computing Systems, vol. 397, Springer, India, pp 685–700Google Scholar
  16. Khan II, Hazarika AK (2012) Study of some water quality parameters of Kolong riverine system of Nagaon, India. Clar Int Multidiscip J 1(2):121–129Google Scholar
  17. Krishnan G, Singh S, Singh RP, Ghosh NC, Khanna A (2016) Water quality index of groundwater of Haridwar District, Uttarakhand, India. Water Energy Int 58(10):55–58Google Scholar
  18. Patel SG, Singh DD, Harshey DK (1983) Pamitae (Jabalpur) sewage polluted water body, as evidenced by chemical and biological indicators of pollution. J Environ Biol 4:437–449Google Scholar
  19. Rehana S, Mujumdar PP (2011) River water quality response under hypothetical climate change scenarios in Tunga-Bhadra River, India. Hydrol Process 25(22):3373–3386CrossRefGoogle Scholar
  20. Saikia MM, Sarma HP (2011) Fluoride geochemistry of Kollong river basin, Assam, India. Arch Appl Sci Res 3(3):367–372Google Scholar
  21. Samantray P, Mishra BK, Panda CR, Rout SP (2009) Assessment of water quality index in Mahanadi and Atharabanki Rivers and Taldanda Canal in Paradip area, India. J Hum Ecol 26(3):153–161Google Scholar
  22. Sebastian J, Yamakanamardi SM (2013) Assessment of water quality index of Cauvery and Kapila Rivers and at their confluence. Int J Lakes Rivers 6(1):59–67Google Scholar
  23. Seth R, Mohan M, Singh P, Singh R, Dobhal R, Singh KP, Gupta S (2014) Water quality evaluation of Himalayan Rivers of Kumaun region, Uttarakhand, India. Appl Water Sci. doi:  10.1007/s13201-014-0213-7
  24. Sharma D, Kansal A (2011) Water quality analysis of River Yamuna using water quality index in the national capital territory, India (2000–2009). Appl Water Sci. doi: 10.1007/s13201-011-0011-4 Google Scholar
  25. Trivedy RK, Goel PK (1986) Chemical and biological methods for water pollution studies. Environmental Publication, IndiaGoogle Scholar
  26. Tyagi S, Sharma B, Singh P, Dobhal R (2013) Water quality assessment in terms of water quality index. Am J Water Resour 1(3):34–38Google Scholar
  27. VishnuRadhan R, Zainudin Z, Sreekanth JB, Dhiman R, Salleh MN, Vethamony P (2015) Temporal water quality response in an urban river: a case study in peninsular Malaysia. App Water Sci. doi: 10.1007/s13201-015-0303-1 Google Scholar
  28. Yadav KK, Gupta N, Kumar V, Sharma S, Arya S (2015) Water quality assessment of Pahuj River using water quality index at Unnao Balaji, M.P., India. Int J Sci Basic Appl Res 19(1):241–250Google Scholar

Copyright information

© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Environmental ScienceGauhati UniversityGuwahatiIndia

Personalised recommendations