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Assessment of Surface Water Quality Using Heavy Metal Pollution Index in Subarnarekha River, India Soma Giri & Abhay Kumar Singh

Water Quality, Exposure and Health ISSN 1876-1658 Volume 5 Number 4 Water Qual Expo Health (2014) 5:173-182 DOI 10.1007/s12403-013-0106-2

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Author's personal copy Water Qual Expo Health (2014) 5:173–182 DOI 10.1007/s12403-013-0106-2


Assessment of Surface Water Quality Using Heavy Metal Pollution Index in Subarnarekha River, India Soma Giri · Abhay Kumar Singh

Received: 4 September 2013 / Revised: 27 November 2013 / Accepted: 3 December 2013 / Published online: 28 December 2013 © Springer Science+Business Media Dordrecht 2013

Abstract Surface water samples were collected from 21 sampling sites throughout the Subarnarekha River during pre-monsoon, monsoon and post-monsoon seasons. The concentrations of Al, As, Ba, Cr, Co, Cu, Fe, Mn, Ni, Se, V and Zn were determined using inductively coupled plasmamass spectrometry for seasonal fluctuation, source apportionment and heavy metal pollution indexing. The results demonstrated that concentrations of the metals showed significant seasonality and most variables exhibited higher levels in the pre-monsoon season. Principal component analysis outcome of four factors together explained 73.13 % of the variance with >1 initial Eigenvalue indicating both innate and anthropogenic activities as contributing factors of metal profusion in the river. To assess the composite influence of all the considered metals on the overall quality of the water, heavy metal pollution indices were calculated. The HPI for surface water of the river considering all the seasons and locations was 32.27. The HPI of the river calculated for the individual locations showed great variations ranging from 3.55 to 388.9. All the locations fall under low to medium classes of HPI except few locations which are under the influence of industries, mining or near the estuary. The enhanced concentrations of certain metals in the Subarnarekha River near industrial and mining establishments may be attributed to anthropogenic contribution from the industrial and mining activities of the area.

Electronic supplementary material The online version of this article (doi:10.1007/s12403-013-0106-2) contains supplementary material, which is available to authorized users. S. Giri (B) · A. K. Singh Geo-Environmental Division (EMG), Central Institute of Mining and Fuel Research, Barwa Road, Dhanbad 826015, India e-mail: [email protected]

Keywords Subarnarekha River · Metals · Source evaluation · Principal component analysis · Seasonal fluctuation · Pollution indexing

Introduction River Water quality monitoring is necessary especially where the water serves as drinking water sources and threatened by pollution resulting from various human activities along the river course (Ahmad et al. 2010; Amadi 2011). Heavy metals contamination in river is one of the major quality issues in many fast growing cities, because maintenance of water quality and sanitation infrastructure did not increase along with population and urbanization growth especially for the developing countries (Karbassi et al. 2007; Akoto et al. 2008; Ahmad et al. 2010). Metals enter into river from variety of sources; it can be either natural or anthropogenic (Wong et al. 2003; Adaikpoh et al. 2005; Akoto et al. 2008). Identification and quantification of these sources should form an important part of managing land and water resources within a particular river catchment (Bellos and Swaidis 2005). Simultaneously, seasonal variations in agricultural activity, storm water runoff, interflow and atmospheric deposition have strong effects on river water quality (Singh et al. 2004; Ouyang et al. 2006; Cidu and Biddau 2007). Thus, characterization of seasonal variability in surface water quality is imperative for evaluating temporal variations of river pollution from natural or anthropogenic contributions. Usually in unaffected environments, the concentration of most of the metals is very low and is mostly derived from the mineralogy and the weathering (Karbassi et al. 2008). Main anthropogenic sources of heavy metal contamination are mining, disposal of untreated and partially treated effluents containing toxic metals as well as metal


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chelates from different industries and indiscriminate use of heavy metal-containing fertilizer and pesticides in agricultural fields (Ammann et al. 2002; Nouri et al. 2006, 2008). Rivers in urban areas have also been associated with water quality problems including metal contamination due to the practice of discharging of untreated domestic and small-scale industrial effluent into the water bodies (Rim-Rukeh et al. 2006; Khadse et al. 2008; Juang et al. 2009; Venugopal et al. 2009; Sekabira et al. 2010). PCA is the most widely used straightforward and quantitatively involved method for transforming a given set of interrelated variables into a new set of variables called the principal components. This powerful method allows identifying the different groups of metals that correlate and thus can be considered as having a similar behaviour and common origin (Tahri et al. 2005). The set of principal components generated presents uncorrelated linear combinations of the original variables and accounts for the total variance of the original data. In this method, all the principal components are generated in such a way that they are orthogonal to each other; hence, correlation between them is zero. Quality indices make use of series of judgments into a reproducible form and compile all the pollution parameters into some easy approach. Several methods have been performed to develop quality indices for estimation of characteristics of surface water with water quality parameters (Horton 1965; Landwehr 1974; Joung et al. 1979; Lohani and Todino 1984; Tiwari and Mishra 1985). There has been considerable development in the area of water quality indices since their inception in the mid 1960s and this work has been thoroughly reviewed by Lumb et al. (2011a). Horton developed a Water Quality Index (WQI) based on eight water quality parameters. Following the seminal model advanced by Horton (1965), Brown et al. (1970, 1971), with support from the National Sanitation Foundation (NSF), USA, proposed an improved version of water quality index model, known as the National Sanitation Foundation Water Quality Index (NSFWQI) model. Among the several offshoots of NSFWQI, one widely published and practiced index is from Oregon state by Cude (2001), named as Oregon water quality index (OWQI). These indexing methods are based on the transformation of water quality parameters into dimensionless values using sub-indices equations and the aggregation of these sub-indices to get a single number for the expression of the water quality. The Canadian Council of Ministers of Environment proposed a Water Quality Index (CCEM-WQI) which was different from NSF-based model and referred as Canadian Water Quality Index (CWQI) in 2001 (Lumb et al. 2011b). In this model, WQI can be assessed based on the frequency of sampling of the variables, the frequency of failed variables and the deviation from the objective given in the standards. United Nations Environmental Program approved CWQI model as a suitable model for assessing the quality of


S. Giri, A. K. Singh

drinking waters and this model has been used by a number of researchers globally (Sarkar and Abbasi 2006; Bharti and Katyal 2011; Lumb et al. 2011a,b; Dede et al. 2013). However, in recent years much attention has been given towards the evaluation of heavy metal pollution in ground and surface waters with development of heavy metal pollution index (HPI) (Reddy 1995; Mohan et al. 1996). The metals monitored for the assessment of the water quality of any system give an idea of the pollution with reference to that particular metal only. HPI is a method that rates the aggregate influence of individual heavy metal on the overall quality of water and is useful in getting a composite influence of all the metals on overall pollution. In the HPI, a similar approach is adopted as Horton (1965) and NSFWQI. However, the weighted factors of the metals correspond to the inverse of the recommended standard of the metal and the summation of the weighted factors does not equal to 1. Moreover, in HPI higher values indicate deteriorated water quality with respect to metals as opposed to other WQI where higher values represent the better quality. In contrast to other WQI, where sub-indices are calculated using only standard values, HPI uses both the ideals values and the standard values. The Subarnarekha River flows through the India’s important industrialized and mining belt known for mining of Copper, Iron ore, Uranium, etc. and production of steel, aluminium, cement, power generation and other related activities. In view of the heavy industrial and mining activities, there is a strong possibility of metal pollution in the Subarnarekha River. The Subarnarekha River is also the main source of urban water supply to many cities like Tatanagar, Muri, Ghatsila, Jaleswar, etc. Considering the above facts, the present study was taken up with the aim of assessing the quality status of the River Subarnarekha with emphasis on metals contamination, their spatio-temporal variations and source identification. This would make it possible to understand the present contamination level and plan management strategy accordingly for the future.

Materials and Methods Site Description The study is carried out in the Subarnarekha River which is the eighth river of India by its flow (12.37 billion m3 /year) and length. It is a rain-fed river originating near Nagri village (23◦ 18 02 N, 85◦ 11 04 E) in the Ranchi district and runs through several major cities and towns covering a distance of about 400 km. It finally joins the Bay of Bengal at Kirtania port (21◦ 33 18 N, 87◦ 23 31 E) in Orissa. The Subarnarekha basin covers an area of 19,300 km2 . This area is nearly 0.6 % of the total national river basin area and yields 0.4 % of the country’s total surface water resources.

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Fig. 1 Map of the study area with sampling locations

The climate of the study area is temperate. Annual rainfall is 1,200–1,400 mm. This area is subject to the southwest monsoon and receives heavy rain (about 80 %) during June– September (monsoon season). Sampling and Analysis Water samples were collected from the river in 21 sites along the Subarnarekha River from its origin to mouth during postmonsoon (September 2011), pre-monsoon (May 2012) and monsoon (July 2012). The sampling season was selected according to the hydrological regime in the basin. The sampling stations are given in Fig. 1. In each site, three replicates were collected and subsequently mixed in situ. Water samples were collected in pre-conditioned acid-washed high-density polyethylene (HDPE) containers. The samples were filtered through pre-washed 0.45 µm Millipore nitrocellulose filters. The initial portion of the filtration was discarded to clean the membrane, and the following ones destined for metal determination were acidified to pH < 2 using suprapure nitric acid and then stored refrigerated in pre-cleaned HDPE bottles until analysis (Radojevic and Bashkin 1999).

Determination of Metals Concentrations of Al, As, Ba, Cr, Co, Cu, Fe, Mn, Ni, Se, V and Zn were determined in water using inductively coupled plasma-mass spectrometry (ICP-MS, Perkin Elmer Elan DRC-e.). A calibration blank and an independent calibration verification standard were analysed for every 15 samples to confirm the calibration status of the ICP-MS. Matrix interference (Blank) was 30). So it can be inferred that the composite influence of all the considered metals on the overall quality of the water is alarming owing to the mineralization, mining and industrial activ-



124.3 17.96

121.9 14.83

ities near some of the locations that can be visualized while evaluating the HPI for each location. The HPI of the surface water of the Subarnarekha River calculated for the individual locations showed great variations. The HPI values ranged from 3.15 to 388.9. Taking into account the average HPI for each location, the lowest HPI


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was calculated for Nagri, which is the origin of the river, while the highest was for Kirtania, which is the mouth of the river. Considering the classes of HPI, all the locations for all the seasons fall under the low class (HPI < 15) to medium class (HPI 15–30) except few locations which are under the influence of industries, mining or near the estuary. The sampling location, Muri, which is under the influence of Aluminium industry, fall in the high class (HPI > 30) for all the seasons. In the pre-monsoon season, the HPI was calculated to be 220.7 which was even higher than the critical value of 100. During the same season, locations of Mosabani U/S of Sankhnalla and Mosabani D/S of Sankhnalla also fall in the high class. These two locations are under the influence of active copper mining. As the river approaches the ocean, the salinity increases and the metal concentration also increases. High salinity creates increased completion between cations and metals for binding sites on clay organic particle surfaces, thus increasing the concentration of metals in the overlying water (Connell and Miller 1984; Elder 1988). So the highest HPI was evaluated for the location at the mouth of the river, Kirtania. For all the three seasons, the HPI fall under the high class (HPI > 30) and exceeded the critical value of 100 also. The other locations near the estuary also experience high metal concentrations like Pontai in pre-monsoon and monsoon season which is depicted from the evaluated HPI.

Conclusions Concentrations of dissolved metals (Al, As, Ba, Cr, Co, Cu, Fe, Mn, Ni, Se, V and Zn) in the surface water of the Subarnarekha River demonstrated great seasonality. Irrespective of the locations, the elemental concentrations were lower in rainy monsoon season as compared to the other seasons due to pronounced dilution effect. When compared to drinking water guidelines established by WHO, India and the US EPA, much greater attention should be paid to Al, As, Cu, Fe, Ni and Se though the concentrations were below the critical values in the monsoon season for some elements. The higher values of metals in the rivers imply additional inputs from unusual geochemical enrichment, which in turn may be attributed to the geological sources coupled with anthropogenic inputs from the catchments. The same is depicted in the calculated HPI also. The HPI ranged from 3.55 to 388.9 with an average of 32.27, falling in the high class. High HPI were observed at few locations near to industries, mining and the estuary. All the other locations fall under low to medium classes of HPI. PCA analysis indicated both innate and anthropogenic activities as contributing factors of metal profusion in the Subarnarekha River. Thus, it is reasonably justified to conclude that the augmented concentrations of metals in water of the Subarnarekha River is greatly influenced by direct discharge of industrial, urban and mining


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wastes into the river and necessitate adequate strategies and management planning to control the intrusion of metals in the river system. Acknowledgments The authors are grateful to Department of Science and Technology, Government of India, for providing the necessary funding under the Fast Track Young Scientist Scheme {Grant No. SR/FTP/ES-185/2010 (G)} for the study. Also, the authors are thankful to the Director and Geo-Environment Division (EMG), Central Institute of Mining and Fuel Research, Dhanbad for providing the necessary laboratory facilities and other logistic support for the study.

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