Application of water quality index for assessment of ...

Water Science & Technology — 60 (8): 2041–2053 © IWA Publishing 2009 DOI:10.2166/wst.2009.537

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@ banerjee et al., 2009


Application of water quality index for assessment of surface water quality surrounding Integrated Industrial EstatePantnagar Tirthankar Banerjee* & Rajeev Kumar Srivastava#

Department of Environmental Sciences, G. B. Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India

E-mail: [email protected]; [email protected] Ph. No. +919648083777 (*Corresponding Author)

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ABSTRACT Water pollution as a consequence of accelerated industrial growth has drawn concerns over public health and environment. In order to assess the extent of environmental impact due to integrated industrial estatePantnagar (IIE-Pantnagar), surface water was monitored for duration of one year. Grab surface water samples from 12 locations were collected, processed and analyzed for 11 pre-identified variables. Besides providing the raw baseline data, the information was normalized and integrated by applying Water Quality Index (WQI). The average surface water quality surrounding IIE-Pantnagar was found to be satisfactory in terms of its potability after conventional treatment and disinfection. During summer season, the WQI of Baigul River at Haldi Road illustrated good water quality (83.3), which however, deteriorates in its downstream at Rudrapur (55.5), signifying moderate quality. The WQI inside IIE-Pantnagar varied from 47.4 to 66.6, revealing moderate to good surface water quality. However, in monsoon and post-monsoon seasons, WQI demonstrated a modest increase in quality for all sampling points, with a few exceptions due to dilution caused by monsoonal rainfall. In this period, average WQI varied from 49.6 to 81.7. During winter season, WQI further declined due to cumulative effects of industrial discharge from IIEPantnagar and other adjacent industrial set-ups coupled with municipal waste water from Rudrapur city. The lowest WQI for entire sampling network was found within IIE-Pantnagar as 37.1, revealing poor water quality. The application of WQI to assess temporal variations in surface water quality was therefore found satisfactory.

Keywords: Industrial pollution; rating curve; surface water; water quality index.

INTRODUCTION Quantitative assessment of water quality is an essential aspect of efficient water resource management. In recent era, evaluation of water quality has become a serious issue because of the grave concern that fresh water will be a scarce resource in the future (Ongley, 1998). Meeting water quality expectations for streams and rivers is also a pre-requisite to protect ground water resources. Rapid industrialization coupled with intensive urbanization, generally initiate deterioration of surface water quality by introducing several organic and inorganic pollutants. Contemporary approaches to evaluate environmental quality are usually based on the comparison of monitored values with their respective standards, but it often becomes difficult to incorporate these standards into a reference scale. Moreover, the overall water quality monitoring occasionally gets tricky due to huge number of samples containing concentrations for numerous parameters (Chapman, 1992). To address the above concerns, the concept of water quality index (WQI) has been developed in many countries and found to be simple as well as effective in evaluating composite water pollution level. The WQI ascribes quality value to an aggregate set of measured parameters. It usually consists of sub-index values assigned to each 3



pre-identified parameter by comparing its measurement with a parameter-specific rating curve, optionally weighted, and combined into the final index (Yagow and Shanholtz, 1996). Bordalo et al. (2006) concluded WQI as an efficient tool for data interpretation. They used a modified nineparameter Scottish WQI to assess the temporal variations of surface water quality and highlighted WQI as a competent tool to reflect aggregate water quality. Parparov et al. (2006) have effectively estimated composite water qualities of two lakes in Israel with the application of WQI for improved water resource management. Avvannavar and Shrihari (2008) determined WQI along the stretch of a river basin to evaluate water quality. In their study, the anthropogenic impacts on river water potability were determined by combining six water quality parameters into WQI. Rapid industrial establishment at IIE-Pantnagar has made enormous ramifications upon the habitability in its surroundings. Industrial effluent from IIE-Pantnagar coupled with municipal waste water from Rudrapur city is directly discharged in to the adjacent surface water which has a huge potential to deplete the water quality and obliterate the adjacent biodiversity. Further, possible heavy metal contamination of surface water may also pose a potential threat to the potability of ground water. Therefore, in the present study, an attempt has been made to evaluate the status of surface water quality, with the application of water quality index for assessing the impacts of industrial and rapid urbanization on adjacent environment. Both spatial and temporal variations in water quality were assessed by means of a score, describing general water quality for twelve different locations surrounding IIE-Pantnagar.

STUDY AREA The centre of experimental site is at Pantnagar industrial estate in Udham Singh Nagar district of Uttarakhand, India. The IIE-Pantnagar is located in the tarai region of Himalaya with latitude & longitude between 28°59´51´´ to 29°01´12´´N and 79°24´9´´ to 79°26´15´´E, respectively. Favorable infrastructural facilities and suitable Uttarakhand industrial policy have helped to attract a large number of industries in this estate. The IIE-Pantnagar has 431 industries, mainly of food production, electroplating, automobile-manufacturing along with their allied types spread over 1310 hectares of land. This industrial area encounters seasonal variations in climate throughout the year with high temperatures in summers (March to June), intense rainfall 4



in monsoons (July to September) and severe cold in winters (October to February). Maximum and minimum temperatures recorded in this region during the year 2007 were 36.2 °C and 6.7 °C, respectively, with an annual rainfall 950 mm.

Selection of surface water quality sampling (SW) locations In order to assess the surface water quality for appropriate risk assessment, twelve different sampling locations were selected on the basis of drainage-pattern, location of probable impact areas and selected effluent discharge points at IIE-Pantnagar and surrounding industrial setup. Figure 1 (a & b) shows surface water sampling locations, while their details have been mentioned in Table 1.

Figure 1 a. Surface water sampling locations surrounding IIE-Pantnagar (Source: Survey of India, Dehra Dun) Water quality monitoring locations were deliberately selected in the North-East & SouthEast directions of IIE-Pantnagar based upon the drainage and surface water flow pattern of the study area.

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N

Century Pulp & Paper Mill, lalkuan

SW 1 SW 2

Pattharchatta River

Sanjay nagar

Baigul River SW 10

SW 11

Pantnagar

IIE-Pantnagar

SW 5

SW 9

Gola River

Kalyani River

IIE-Pantnagar

SW 12

SW 7

Sugar Mill, Kichha

SW 6 Baigul River Rudrapur

SW 4

Legends Industrial setup Surface water sampling location Town Flow of river

Beni River

Gola River

SW 8 Kichha SW 3

Figure 1 b. Surface water sampling locations surrounding IIE-Pantnagar

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Shantipuri No. 1, Lalkuan (SW1) is the sampling location over a small stream that primarily carries industrial discharge from a separate industrial setup of Century Pulp and Paper Mill, Lalkuan (Century, Lalkuan), near to IIE-Pantnagar. Although this sampling location itself represents an upstream, but exhibits a heavy pollution load. It confluences with the Gola River after Sanjaynagar, contributing substantial increase of pollution in the receiving water body. Gola River at Sanjaynagar (SW2) is an upstream sampling location in the mountain river Gola. The river bed consists of sand and pebbles along with some rocks protruding from the water. The river basin undergoes heavy dredging during dry seasons, but the flow of water radically increases during monsoons. Gola River near Kichha Dam (SW3) is a downstream sampling location that exhibits heavy pollution load. The color of water remains blackish in most of the seasons, with intense objectionable odour primarily due to the presence of industrial effluents from IIE-Pantnagar; Century, Lalkuan and sugar mill, Kichha. Baigul River at Rudrapur (SW4) is a downstream surface water sampling point in the river Baigul. Both Baigul (SW10) and Pattharchatta River (SW11), Haldi, previously selected as upstream sampling locations, ultimately confluence into Baigul River near Rudrapur. These are responsible for carrying industrial effluents from IIE-Pantnagar and municipal sewages from Rudrapur town. The water is highly turbid, looks brownish and seems critically polluted. Untreated industrial effluents coupled with municipal discharge produce considerable impacts on the water quality which is further deteriorated due to adjacent commercial activities. SW5, 6, 7, 9 and 12 represent sampling locations inside IIE-Pantnagar. These locations were selected based on the intersections where underground drainage pipes carrying effluents from several industries discharge in to surface rivers. More specifically, SIDCUL-2 (SW6) demonstrates the sampling point along the border of the industrial estate, responsible for discharge of substantial portion of the industrial effluent. The remaining portion of effluent is discharged through the river at SIDCUL-3 (SW7). Both these locations exhibit considerable amount of pollution during all the sampling seasons. SW8 represents downstream sampling point at Beni River, Kichha. The river at this location is highly polluted due to the industrial discharge both from IIE-Pantnagar and sugar mill, Kichha.

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SW10 & 11 represent two identical upstream sampling locations at Baigul River, Haldi Road and Pattharchatta River, Haldi, respectively. These sampling locations were preferably selected as both the rivers intersect at IIE-Pantnagar, receive industrial effluents and then drain out. Location SW10 represents the upstream of the Baigul River which collects effluents from SW9 and SW12, draining out from locations SW7 & SW6. In contrast, location SW11 is at the upstream of Pattharchatta River that receives effluents at SW5 & SW12, finally draining out at location at SW6.

Table 1. Surface water sampling locations surrounding IIE-Pantnagar Sl. No.

Sampling location

Source

Distance from centre of IIE-Pantnagar (km)

Direction from centre of IIE-Pantnagar

SW1.

Shantipuri No. 1, Lalkuan

Stream

13.8

NE

SW2.

Gola River, Sanjaynagar

Gola River

15.9

ENE

SW3.

Kichha dam

Gola River

15.8

SE

SW4.

Baigul River, Rudrapur

Baigul River

4.9

S

SW5.

SIDCUL- 1

River

2.8

N

SW6.

SIDCUL- 2

River

2.5

SE

SW7.

SIDCUL- 3

River

2.4

E

SW8.

Beni River, Kichha

Beni River

12.0

SE

SW9.

SIDCUL- 4

Kalyani River

3.0

NE

SW10.

Baigul River, Haldi Road

Baigul River

6.1

NE

SW11.

Pattharchatta River, Haldi

Pattharchatta River

4.1

NNE

SW12.

SIDCUL- 5

River

1.2

ENE

MATERIALS AND METHODS Sampling and analysis of water samples Grab samples were collected at twelve pre-identified sampling locations once in each of the four seasons i.e. summer (April, 07), monsoon (July, 07), post-monsoon (September, 07) & winter (December, 07). Adequate precautions were taken for quality assurance of the collected water samples. All the plastic wares (Tarsons, India) and glass wares (Borosil, India) were previously washed with detergent solution, rinsed with water, soaked for 48 h in 50% HNO3 and finally rinsed thoroughly with deionized water. For determination of specific water pollutants, samples were collected in sterilized plastic bottles, refrigerated and analyzed within 24 h of collection. Samples for estimating dissolved oxygen concentration were collected separately in 8



300 ml BOD glass bottles (Borosil, India) and adequate precautions were taken in order to reduce any possibility of air trapping. All reagents were of analytical grade purchased from E. Merck (Germany), Hi Media (India) & S.D. Fine chemicals (India). However, pH was analyzed at the respective locations itself and for remaining parameters, water samples were analyzed at the laboratory as per standard procedures outlined in APHA (1998) and IS: 3025 (1964).

Water Quality Index (WQI) The WQI is a numeric expression of water quality which attempts to provide an integrated mechanism for describing a certain level of cumulative water quality and is useful for comparative analysis (Bordalo et al., 2006). The selection of the parameters to compute WQI depends upon several factors such as purpose of the index, significance of the water quality variables and data availability (Pesce and Wunderlin, 2000; Stigter et al., 2006). In the present study, surface water was monitored for the evaluation of pollution level with respect to its utilization as a drinking water source with conventional treatment after disinfection (IS: 22961982, Class C). Therefore, parameters such as pH, total dissolved solids (TDS, mg/L), hardness (mg/L), chlorides (Cl, mg/L), sulphates (SO4, mg/L), nitrate (NO3, mg/L), fluorides (F, mg/L), phosphate as orthophosphate (H3PO4, mg/L), dissolved oxygen (DO, mg/L), biochemical oxygen demand (5 days at 20°C) (BOD5, mg/L) and total coliform organisms, (MPN/100ml) were found adequate in order to create a reliable, unbiased and functional WQI. The pH of surface water is one of the most significant and operational water quality parameter, while TDS & Cl are the potential indicators of water salinity. Nitrate, sulphates and fluorides are the probable contaminants of the surface water and were therefore, considered logical for developing WQI. Extreme levels of sulphate cause dehydration and gastrointestinal irritation; toxicity of nitrate is mainly attributed through its reduction to nitrite and is responsible for methaemoglobinemia and stomach cancer (Stigter et al., 2006), while excess levels of fluoride cause mottling of teeth. The DO, MPN & BOD5 are very crucial water quality variables for expressing the organic load and microbial contamination of surface water and were therefore considered for computing WQI. Further, the importance of considering orthophosphate and hardness of surface water has been illustrated ahead. Therefore, these 11 pre-identified water quality parameters broadly represent all three broad categories viz. physical, chemical and biological. Detailed characterization and variations in surface water quality within the entire study period have been provided in Table 2, 9



while the results of pooled season correlation of the observed data have been illustrated in Table 3. Table 2. Statistical characterization of data set for the computation of WQI Parameters DO (mg/L) BOD5 (mg/L) pH TDS (mg/L) Hardness (as CaCO3) (mg/L) Chloride (mg/L) Sulphate (as SO4) (mg/L) Nitrate (as NO3) (mg/L) Fluoride (as F) (mg/L) Phosphate (as H3PO4) (mg/L) Total Coliform (MPN/100ml)

Min. 0.0 9.4 4.64 191.0 50.0 5.0 26.4 2.3 0.0 0.4 28

Max. 8.9 720.0 8.33 6102.0 654.0 886.0 435.0 55.3 4.3 18.6 1100

Median 4.2 63.0 6.78 425.0 262.0 30.5 169.9 18.0 2.0 2.5 210

Mean 4.1 119.0 6.72 703.3 287.1 97.1 192.2 23.7 2.0 4.0 352

St. Dev.a 2.7 162.1 1.01 1153.5 119.6 191.6 107.3 16.1 1.0 4.2 385

C.V.b 0.7 1.4 0.15 1.6 0.42 2.0 0.6 0.7 0.5 1.0 1.1

Note: a Standard deviation. b Coefficient of variation (St. Dev./Mean) Rating Curves In this study, a simple numeric scale related with the degree of quality was found appropriate for assessing the variations in water quality. The methodologies adopted were in line with the other investigators (Bhargava, 1983; Pesce and Wunderlin, 2000 and Liou et al., 2004) for representing water quality in a comprehensive manner. Observations for each of the pre-identified parameters were converted into values on an interval scale ranging from 0 to 100, in accordance with the extent of water quality from worst to excellent. From a consideration of permissible and desirable limits of surface water quality, the suggested values for pre-identified variables along with sensitivity functions (Pi) have been illustrated in Table 4 and Figure 2. Table 3. Correlation matrix of the observed data for the pre-identified parameters DO

DO BOD5 pH TDS Hardness Chloride Sulphate Nitrate Fluoride Phosphate

MPN

BOD5

pH

TDS

Hardness

Chloride

Sulphate

Nitrate

Fluoride

Phosphate

MPN

1.000 -0.683*

1.000

0.064

-0.437

1.000

-0.073

0.169

-0.549

1.000

-0.114

0.135

-0.349

0.604*

1.000

-0.480

0.398

-0.527

0.419

0.141

1.000

-0.290

0.338

-0.104

-0.060

0.374

0.178

1.000

-0.590*

0.800**

-0.248

0.451

0.213

0.421

0.423

1.000

-0.334

0.491

-0.520

-0.085

0.194

0.179

0.107

0.014

1.000

-0.383

0.451

-0.427

-0.060

-0.157

0.746

0.191

0.182

0.377

1.000

0.749**

0.782**

-0.358

-0.012

0.019

0.646*

0.141

0.490

0.461

0.679*

1.000

Note: * 5% level of significance. ** 1% level of significance Table 4. Sensitivity functions and water quality ratings for different parameters 10


Water Science & Technology — 60 (8): 2041–2053 © IWA Publishing 2009 DOI:10.2166/wst.2009.537 Parameters DO (mg/L) BOD5 (mg/L) pH TDS (mg/L) Hardness (as CaCO3) (mg/L) Chloride (mg/L) Sulphate (as SO4) (mg/L) Nitrate (as NO3) (mg/L) Fluoride (as F) (mg/L) Phosphate (as H3PO4) (mg/L) Total Coliform (MPN/100ml)

Sensitivity functions (Pi) 4 3 1 2 1 1 2 2 2 2 3

Water quality ratings Excellent (100) ≥8.0 ≤1.0 6.5-8.5 ≤500 ≤50.0 ≤250.0 ≤200.0 ≤45.0 ≤1.0 ≤1.0 0

Good (80)

Satisfactory (60)

6.0 2.0 1000 150.0 400.0 300.0 47.0 1.25 3.0 500

4.0 3.0 1500 300.0 600.0 400.0 50.0 1.5 5.0 5000

Poor (40) 3.0 10.0 2000 400.0 800.0 700.0 55.0 2.0 7.0 6000

Worst (0) ≤2.0 ≥30.0 6.5-8.5 ≥2100 ≥600.0 ≥1000.0 ≥1000.0 ≥60.0 >2.0 ≥10.0 ≥8000

In order to evaluate the potability of surface water after conventional treatment and disinfection, the tolerance limits prescribed by the Bureau of Indian Standards, New Delhi (IS: 2490-1981; IS: 2296-1982, Class C and IS: 10,500-1991) were adopted. However, considering these water quality standards to develop rating curves, some limitations regarding the tolerance limits for all the pre-identified pollutants were encountered. Therefore, for some specific pollutants viz. hardness (as CaCO3) and phosphate (orthophosphate as H3PO4), respective standards from additional sources were considered in order to enhance the reliability of the index. Hardness is an essential water quality indicator and its contribution to WQI is unquestionable. All house cleaning activities ranging from bathing and grooming to dishwashing and laundering are difficult with water having hardness more than 300 mg/L (Masters, 2004). Therefore, in case of surface water, the standard values of 300 and 600 mg/L were considered as satisfactory and worst, respectively. Phosphate is very often the limiting nutrient and its concentration in the stream is important when eutrophication is under consideration. Apart from carbon (usually available in water from a number of sources), nitrogen and phosphorous are often considered as limiting nutrients for aquatic species. It is therefore, necessary to incorporate the tolerance standards, both for nitrogen (as nitrate) as well as phosphorus (as ortho-phosphate), for the computation of WQI. The tolerance limit of NO3 for class ‘C’ water has already been proposed by the Bureau of Indian Standards, New Delhi (IS: 2296, 1982), but there is lack of any prescribed threshold limit for phosphate in inland surface water. However, the prescribed limit for disposal of dissolved phosphate as an effluent in inland surface water is recommended as 5 mg/L (phosphate as P) (IS: 2490-1981).

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Figure 2. The assigned rating curves (a-k) for various pre-identified parameters

Although, the possibility of cultural eutrophication with enhanced accumulation of phosphate (lotic) is very less in running water than lentic water, but several researchers have raised concern over the possibility. Sawyer (1947) and Vollenweider (1975) concluded that 12



phosphorus concentration in excess of 15 µg/L and 20 µg/L, respectively are sufficient to cause phosphorus enrichment in lentic system. According to Shock and Pratt (2003), nuisance algal blooms in surface water are stimulated by an average phosphate concentration of 80 µg/L. Therefore, pertaining adequate margin of safety for lotic systems, a threshold value of 5 mg/L for phosphate is considered satisfactory for the computation of WQI. In this study, however, with certain exceptions, a water quality rating of ‘0’ was allotted to the concentrations which exceeded or equaled the standard value recommended for the discharge of industrial effluents into surface water (IS: 2490-1981). This indicates that for individual parameter, water quality of surface water will be considered worst if concentration of the pollutant exceeds or equals the prescribed standards for discharge of effluents. In case of nitrate, pH, DO, hardness, phosphate and MPN, specific standards were recommended considering adequate margin of safety for inland surface water. A water quality rating of ‘60’ was proposed for the respective pollutants after considering tolerance limits for inland surface water subjected to pollution (IS: 2296-1982, Class C). Excellent water quality rating (100) was considered for variable concentrations which were lower than or equal to the desirable standards for drinking water (IS: 10,500-1991).

Computation of WQI The WQI provides a mechanism to present cumulatively derived numerical expression defining levels of water quality. The WQI approach has many variants in the literature for comparing various physico-chemical and biological parameters (Liou et al., 2004; Bordalo et al., 2006; Avvannavar and Shrihari, 2008). Horton (1965) considered ratings and weightings of variables, Brown et al. (1972) proposed multiplicative index, Bhargava (1983) and Dinius (1987) developed weighted geometric means, Smith (1990) used minimum operator concept, while Pesce and Wunderlin (2000) presented weighted averaging method to compute overall WQI. In the present experiment, the concept of weighted average (Conesa, 1995; Pesce and Wunderlin, 2000) was used to compute overall WQI because of the simplicity involved in data handling, minimal data processing and flexibility for use under different environmental conditions. The weighted average also provides adequate depression in the WQI values due to low sensitivity function value for variables. The simplified mathematical expression for WQI is given by equation (1) 13


Water Science & Technology — 60 (8): 2041–2053 © IWA Publishing 2009 DOI:10.2166/wst.2009.537 n

C P WQI =

i 1 n

i i

(1)

P i 1

i

where Ci represents assigned quality index for individual parameter from the rating curves (Figure 2, a-k). The Pi as illustrated in Table 4, are the sensitivity functions allocated to each pre-identified parameter. The sensitivity function is the relative weight from 1 to 4, which has been assigned to each parameter for surface water pertaining to its importance for drinking purpose after conventional treatment and disinfection. The sensitivity function 4 denotes a parameter that is most significant for surface water (e.g. dissolved oxygen), while the function 1 corresponds to a parameter that has a minimal impact (e.g. Chloride). The computed WQI for different sampling locations and seasons were then compared with cumulative WQI ranges, proposed for WQI categorization (Table 5). However, application of WQI for determination of composite surface water quality also poses some limitations. Quality of surface water can be variable depending on the purpose for which the water is used. A river may be considered as polluted if it poses minimum DO level, but the same river may be found adequate for irrigation purpose, where parameter such as TDS, Cl and sodium ratio are the most relevant quality variables. Therefore, in order to develop an effective WQI, it is imperative to specifically consider relevant parameters & standards depending upon the application involved.

Table 5. Cumulative water quality index ranges of surface water WQI 0 >0 -