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Application of water quality index for assessment of pond water quality status in Orissa, India

Rizwan Reza1*and Gurdeep Singh1

1Deparment of Environmental Science and Engineering, Indian School of Mines, Dhanbad, Jharkhand India

DOI:http://dx.doi.org/10.12944/CWE.5.2.13

水quality index (WQI) has been calculated for different Ponds water in industrialized area at Angul-Talcher region of Orissa (India). Water Quality Index used to assess the spatial and temporal variation and also helps in classification of pond water suitability. The pond water samples were collected (n=14) from different locations of the study area. The highest value of WQI of the samples was 80 in monsoon season while the lowest value was 43 in summer season. The higher value of WQI has been found to be mainly from the increased concentration of BOD and Coliforms in the pond water. Most of the water samples within study area were found within medium category of National Sanitation Foundation Water Quality Index (NSF-WQI).


Pond Water; WQI; Seasonal Variation; National Sanitation Foundation; Domestic Waste

Copy the following to cite this article:

Reza R, Singh G. Application of water quality index for assessment of pond water quality status in Orissa, India. Curr World Environ 2010;5(2):305-310 DOI:http://dx.doi.org/10.12944/CWE.5.2.13

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Reza R, Singh G. Application of water quality index for assessment of pond water quality status in Orissa, India. Curr World Environ 2010;5(2):305-310. Available from://www.a-i-l-s-a.com?p=289/


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Article Publishing History

Received: 2010-06-05
Accepted: 2010-07-11

Introduction

水Quality Monitoring data consists of routine measurements of physical, chemical and biological variables that are intended to give insight into aquatic environment. However, it does not give the overall picture on the water quality status. Further need to translate it into a form that is easily understood and effectively interpreted. In general, water quality indices incorporate data from multiple water quality parameters into a mathematical equation that rates the health of a stream with a single number. That number is placed on a relative scale that rates the water quality in categories ranging from very bad to excellent (Stambuk-Giljanovic, 1999; Bordalo et al., 2001). WQI also permits the assessment of changes in water quality and its trends. On the basis of the results of WQI, pond water can be classified for the suitability of various uses (Cude, 2001; Chapman, 1992). Some of the water quality index approaches that havebeen frequently employed in public domain for the purpose of water quality assessment (Said et al., 2004). US National Sanitation Foundation Water Quality Index, NSFWQI (Brown et al. 1970), Canadian Water Quality index (CCME, 2001), British Columbia Water Quality Index, BCWQI (Zandbergen and Hall, 1998), Oregon Water Quality Index, OWQI (Cude, 2001) and the Florida Stream Water Quality Index, FWQI (SAFE, 1995) are very frequent used water quality indices in various part of the world.The water quality index (WQI) has been considered as the criteria for water classification, based on the use of standard parameters for water characterization. The index is a numeric expression used to transform large quantities of water characterization data into a single number, which represents the existing water quality level (Sanchez et al., 2006; Bordaloet al., 2006). A commonly used WQI was developed by the National Sanitation Foundation (NSF) in 1970 (Brownet al., 1970).

The NSF WQI was developed to provide a standardized method for comparing the water quality, based upon various water quality parameters. The WQI can be calculated with less than nine parameters (Brown et al., 1970). In present study the data matrix was obtained from various season (summer, monsoon, post-monsoon and winter), to investigate the spatial and temporal variation in water quality and classify the pond water status. According to the Indian standard (CPCB), PH, DO, BOD and Coliform are the major parameters to describe the suitability of water for various purposes.

Material and Methods

The Angul-Talcher area lies between latitudes 20° 37’ N to 21° 10’ N and longitudes 84º 53’ E to 85° 28’ E (Fig. 1) and situated at an average height of 139 meters above mean sea level (MSL). The area is characterized by tropical climate and has many important natural resources, which include coal, forests, fertile land, minor minerals, ground and surface water, etc. At present Nalco Smelter and its Captive Power Plant (CPP-960 MW), Super Talcher Thermal Power plant (STTPS-3000MW), Talcher Thermal Power Plant (TTPS-460MW), Iron & Steel industries and several Coal Mines are the major industries operating there. Beside that the agriculture is also covered a major portion of land in Angul. The overall area is intensively farmed and industrialized. These industries can increase the pollution load on the various components of environment in surrounding areas including pond water in surrounding areas. Geographical location of the study area is shown in Figure 1.

The water samples were collected from twelve (12) different locations in all four seasons viz. summer, monsoon, post-monsoon and winter season. Sampling locations were selected on the basis of different land use pattern (agricultural, residential and barren etc.). Care was taken to collect subsequent samples from same location in all season. The Samples were taken from 10 to 15cm below the water surface using acid washed plastic container to avoid unpredictable changes in characteristic as per standard procedures (APHA, 1998). Details of sampling locations along with their
latitude and longitude are illustrated in Table 1.

水Quality Index

水Quality Monitoring data consists of routine measurements of physical, chemical and biological variables that are intended to give insight into aquatic environment. However, it does not give the over picture on the water quality. There a further need to translate it into a form that is easily understood and effectively interpreted. Water Quality Index (WQI) plays an important role in such translation process.

For calculation of water quality index (WQI), the raw analytical results for the selected water quality variables, having different units of measurements, are transformed into unit-less sub-index values (Cude, 2001). This can be done by transforming each parameter into 0 to 100 scales (Pesce and Wunderlin, 2000) by using sub-index curves. The sub-index curves may be linear, non-linear. These sub-indices are then averaged to give a water quality index value (Cude, 2001) applying suitable weighting factors that reflect the importance of each parameter as an indicator of the water quality (Pesce and Wunderlin, 2000).

美国国家卫生基金会水质指数(1974) has been widely field tested and applied to data from a number of different geographical areas. It has following mathematical structure.

where, Iiis the sub index for ithwater quality parameter,
W
iis the weight (in terms of importance) associated with ithwater quality parameter,
n is the number of water quality parameters.

The following equations were used to calculate the WQI Sub-Indices:

  1. % Saturation DO (0-40%) = 0.18 + (0.66 * % Saturation DO)
  2. % Saturation DO (40-100 %) = (-13.5) + (1.17 * % Saturation DO)
  3. % Saturation DO (100-140%) = 163.34 - (0.62 * % Saturation DO)
  4. BOD (0 to 10 ppm) = 96.67 - (7 * BOD)
  5. BOD (10 to 30 ppm) = 38.90 - (1.23 * BOD)
  6. BOD (>30ppm) = 2.0
  7. pH值(5.0 - 7.3)= (-142.67)+ (33.50 * pH值)
  8. pH (7.3 to 10.0) = 316.96 - (29.85 * pH)
  9. Coliforms (1 to 1000) = 97.20 - (26.80 * log [MPN])
  10. Coliforms (1000 to 10000) = 42.33 - (7.75 * log [MPN])

In deciding the weights of parameters incorporated in the index system it was believed that the importance of each parameter depends upon the degree of which the parameter can manifest changes in water quality. It was also assumed that a policy of natural land use in the basin was a possible choice for agro-industrial development in the province. The weights of the parameters are given in Table 1. The four parameters which are considered important (especially for the Indian conditions as described by CPCB) for NSF-WQI have following weights as
  1. % Saturation Dissolved Oxygen = 0.31
  2. Fecal Coliform = 0.28
  3. pH = 0.22
  4. BOD = 0.19

In general, water quality index is mathematical equation that rates the health of a stream with a single number. That number is placed on a relative scale that rates the water quality in categories ranging from very bad to excellent. Based on the estimated value of WQI, the water is categorized as per Table 2.

Results and Discussion

美国国家卫生基金会水质指数(NSF-WQI) was used to assess the water quality status of various ponds. The tool converts the water quality data into usable information which express the level of water quality.

The values of water quality index were higher during monsoon season followed by winter, post-monsoon and then summer season. The values of WQI showed significant changes between different sampling locations and the analogous trend of seasonal variation. The higher values in monsoon season due to dilution impact of concentration of related parameters (Phiri et al., 2005). While the lower value in summer season was due to elevation of water temperature which leads to increase the evaporation rate and the accumulation of concern dissolved pollutants in water (Pandey et al., 1999). The figures (Fig. 2) clearly indicate the variation of water quality index (WQI) with respect to various seasons.

Table 1: Details of pond water sampling location along with their longitude and latitude

Sample Code

Locations

Longitude

Latitude

W-11

Manapur pond water near Beragadia village

85016’30"

20051’11"

W-12

Hakimpara pond water near Jagannath temple

85006’06"

20049’31"

W-13

Kumanda village, pond water

85005’02"

20051’15"

W-14

Vikash nagar, Angul, pond water

85007’04"

20050’42"

W-15

Raniguda village, pond water

85007’00"

20051’28"

W-16

Kandasaar village pond water

85008’36"

20050’15"

W-17

Derjang reservoirs

85001’57"

20051’02"

W-18

Pond water near Manapur village

85014’10"

20051’29"

W-19

Pond water near Jagannathpur village

85011’30"

20055’48"

W-20

Pond water near Turang village

85007’30"

20050’50"

W-21

Nuashahi pond water

85011’14"

20047’50"

W-22

Tulashi pal pond water

85011’12"

20048’14"

W-23

Longibeda pond water

85010’51"

20048’37"

W-24

Gadara khai pond water

85010’11"

20049’04"

Figure 1: Study area along with the sampling locations
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Figure 2: Seasonal variation in WQI at various locations
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表2:描述符类of NSF-WQI values

NSF-WQI

Descriptor Category

0 to 25

Very Bad

26 to 50

Bad

51 to 70

Medium

71 to 90

Good

91 to 100

Excellent


The WQI of water samples were found in the range of 43 to 76 in summer, 62 to 80 in monsoon, 44- to 61 in post-monsoon and 52 to 72 in winter season. On the basis of descriptive categories of NSF-WQI values, 21% of samples were found in Good category while the 66% of the samples were in medium category. Only 13% of samples were found in bad category. The lowervalue of WQI of various samples (W-11, W-16, W- is attributed to the high evaporation rate and the accumulation of concern dissolved pollutants in water during summer season (Pandey et al., 1999). While in the case of post-monsoon season, few samples were found within bad category. It may be due to settled or un-dissolved waste material carried out by run-off from rural area, having no roper sanitation. The main sources of pollution were defined as high BOD and Coliforms, which were considerably increased due to the domestic waste, improper sanitation and back water from agriculture fields (Rim-Rukeh et al., 2006; Izonfuo and Bariweni, 2001).

Table 3: Season wise statistical variation in WQI values

Seasons

Minimum

Maximum

Mean

Std. Deviation

Summer

43

76

57

9

Monsoon

62

80

72

6

Post-monsoon

44

61

52

4

Winter

52

72

62

5


Conclusions

The WQI for all samples were found in the range of 43 to 80. Almost 87% of the total water samples were found in range of good and mediumcategory of National Sanitation Foundation Water Quality Index (NSF-WQI). Out of 56 water samples only seven (13%) were found within bad category, it is due to domestic activities such as bathing of men and cattles, washing utensil etc. were frequently carried out along with the various pond. BOD and fecal coliforms are the main water quality sub-indices that reflect the impact of anthropogenic activities on the water quality of this river system.

Acknowledgements

The authors are thankful to State Pollution Control Board Orissa, for sponsoring this study. Authors are also grateful to Prof. T. Kumar, Director, ISM for granting of ISM fellowship. One of the authors (Rizwan Reza) is grateful to Indian School of Mines/MHRD/Govt. of India, for providing research facilities.

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