Evaluation of water quality using water quality index ...

Science of the Total Environment 584–585 (2017) 131–144

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey) Şehnaz Şener a, Erhan Şener b,⁎, Ayşen Davraz a a b

Süleyman Demirel University, Department of Geological Engineering, Çünür, TR-32260 Isparta, Turkey Süleyman Demirel University, Remote Sensing Center, Çünür, TR-32260 Isparta, Turkey

H I G H L I G H T

G R A P H I C A L

A B S T R A C T

• This study aims to evaluate water quality of the Aksu River. • The water quality for drinking purpose was evaluated using water quality index (WQI) method. • The effect of pollutants is dominant on to water quality in the region. • COD and Mg are the most effective water quality parameters.

a r t i c l e

i n f o

Article history: Received 19 October 2016 Received in revised form 13 January 2017 Accepted 16 January 2017 Available online xxxx Editor: D. Barcelo Keywords: Aksu River Drinking water WQI GIS

a b s t r a c t The aim of this study is evaluate water quality of the Aksu River, the main river recharging the Karacaören-1 Dam Lake and flowing approximately 145 km from Isparta province to Mediterranean. Due to plan for obtaining drinking water from the Karacaören-1 Dam Lake for Antalya Province, this study has great importance. In this study, physical and chemical analyses of water samples taken from 21 locations (in October 2011 and May 2012, two periods) through flow path of the river were investigated. The analysis results were compared with maximum permissible limit values recommended by World Health Organization and Turkish drinking water standards. The water quality for drinking purpose was evaluated using the water quality index (WQI) method. The computed WQI values are between 35.6133 and 337.5198 in the study. The prepared WQI map shows that Karacaören-1 Dam Lake generally has good water quality. However, water quality is poor and very poor in the north and south of the river basin. The effects of punctual and diffuse pollutants dominate the water quality in these regions. Furthermore, the most effective water quality parameters are COD and Mg on the determination of WQI for the present study. © 2017 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail addresses: [email protected] (Ş. Şener), [email protected] (E. Şener), [email protected] (A. Davraz).

http://dx.doi.org/10.1016/j.scitotenv.2017.01.102 0048-9697/© 2017 Elsevier B.V. All rights reserved.

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Ş. Şener et al. / Science of the Total Environment 584–585 (2017) 131–144

1. Introduction The surface water quality is very sensitive and critical issue in many countries. Also, with an increased understanding of the importance of drinking water quality to public health and raw water quality to aquatic life, there is a great need to assess surface water quality (Ouyang, 2005). Anthropogenic influences as well as natural processes degrade surface waters and impair their use for drinking, industry, agriculture, recreation and other purposes (Carpenter et al., 1998; Jarvie et al., 1998; Simeonov et al., 2003; Sánchez et al., 2007; Kazi et al., 2009). So a water quality monitoring program is necessary for the protection of fresh water resources (Pesce and Wunderlin, 2000). The geochemical study of river basins reveals the nature of the geochemical factors, which helps us to understand the exogenic cycles of elements in the continent–river–ocean system (Giridharan et al., 2010). The hydrogeochemical properties of water are an important factor determining its use for domestic, irrigation and industrial purposes. Interaction of water with lithologic units through which it flows greatly controls the water chemistry and quality (Subramani et al., 2009). Several approaches have been introduced to assess the water chemistry and status of water quality in the river (Afsin, 1997; Subramani, 2005; Möller et al., 2007; Subramani et al., 2005; Shastry et al., 1972; Aston et al., 1974; Lizcano et al., 1974; Nunes et al., 2003; Tsegaye et al., 2006). Tsegaye et al. (2006) evaluated the impact of land use/land cover changes, seasonal, and location on water quality of streams within the Wheeler Lake Basin in northern Alabama. Yidana and Yidana (2010) used conventional graphical methods with multivariate statistical methods and GIS to study the controls on the hydrochemistry and the severity of the controlling factors at different locations in the flow system. Also, they used water quality index (WQI) method to assess the suitability of groundwater from the study area for human consumption. Kannel et al. (2007) used WQI to evaluate spatial and seasonal changes in the water quality in the Bagmati river basin. Debels et al. (2005) calculated WQI in order to characterize the spatial and temporal variability of surface water quality in the basin, from nine physicochemical parameters, periodically measured at 18 sampling sites in the Chill'an River. The use of a WQI was initially proposed by Horton (1965) and Brown et al. (1970). Then, many different methods for the calculation of WQI's have been developed by several authors (Debels et al., 2005; Saeedi et al., 2009; Tsegaye et al., 2006). The WQI has been considered as one criterion for surface water classifications based on the use of standard parameters for water characterization. It provides a comprehensive picture of the quality of water for most domestic uses. WQI is a mathematical instrument used to transform large quantities of water characterization data into a single number, which represents the water quality level (Bordalo et al., 2006; Sánchez et al., 2007). Numerous water quality indices have been formulated all over the world such as US National Sanitation Foundation Water Quality Index (NSFWQI) (Brown et al., 1970), Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI) (Khan et al., 2003), British Columbia Water Quality Index (BCWQI), and Oregon Water Quality Index (OWQI) (Debels et al., 2005; Kannel et al., 2007; Abbasi, 2002). The most of these indices are based on the WQI developed by the U.S. National Sanitation Foundation (NSF) in 1970 and it is commonly used in the world (Brown et al., 1970). The NSF WQI was developed to provide a standardized method for comparing the water quality of various water sources based upon nine water quality parameters, i.e., temperature, pH, dissolved oxygen, turbidity, fecal coliform, biochemical oxygen demand, total phosphates, nitrates and total solids. The water quality ranges have been defined as excellent, good, medium, bad and very bad according to NSF WQI method (Chaturvedi and Bassin, 2009). The Aksu River has great importance for the region. It is the main river that recharge to the Karacaören-1 Dam Lake. Furthermore, local authority planned to taken water from Karacaören-1 Dam lake to supply drinking and municipal water requirement for Antalya city. However, there are important punctual and diffuse pollutants which are affected

river water quality. Domestic wastewaters of the settlements and agricultural activities are the most important diffuse pollution sources. In addition, leather industry, marble factories and fish farms are punctual pollution sources in the study area. The purified wastewater of Isparta Province treatment plant is discharged into the Aksu River in the north part of the river basin. Even if wastewater treatment plant of the Isparta Province is working full capacity, 60,000 m3/day of wastewater reaches the Aksu River. Occasional failures and operational faults can be experienced at this wastewater treatment plant, causing pollutants to enter the river. In addition, the Sav town wastewater is discharged without treatment into the Aksu River in this region. Wastewater samples, which were taken from the discharge point into the Aksu River and analysed in this study, and the Cl (10,477 ppm), Cr (20.218 ppm), P (16.851 ppm) and SO4 (9691.79 ppm) were detected at high concentrations. Additionally, there is an industrial leather wastewater treatment plant in Isparta Province. From this facility, domestic and industrial wastewater is discharged into the Aksu River at 800–1200 m3/day. A great number of fish farm situated on tributaries of the Aksu River and Karacaören Dam are discharge into the Aksu River. The increases in nitrate, nitrite, orto-phosphate and total phosphorus contents of the river water were determined in a study on the effect of water quality on fish farms in the tributaries of the Aksu River (Bulut et al., 2012). The fish farms negatively affect the river water quality. In another study, Cryptosporidium parvum (C. parvum) and Giardia intestinalis (G. intestinalis) parasites were determined in water samples around the Aksu River flowing from Isparta Province centre to the Karacaören Dam (Rad et al., 2007). C. parvum and G. intestinalis were determined at 40% and 50%, respectively. In this region, there are settlement areas where people are interested in agriculture and raising animals, refinery plants and garbage areas. Contamination sources of these parasites are sewage treatment plant effluent, septic tank discharge, and infected pets, farm and wild animals (Rad et al., 2007). In addition, agricultural activity is intensive along the Aksu River. During these activities, fertilizers (synthetic and natural) and pesticides are used extensively to increase product quality and quantity. Farmyard manure is used more intensively than synthetic fertilizer. In addition, nitrogen (pure N), phosphorous (P2O5) and potassium (K2O) are used in agricultural activities (Anonymous, 2015a, 2015b). The punctual and diffuse pollutants are shown in Fig. 2. Currently, effects of these pollutants on water quality are not exactly known. Hence, this study is extremely important for the region. The main objectives of this study are (1) to determine geological and hydrogeological properties of the river basin, (2) to assess the physicochemical properties of the river water and (3) to determine the water quality of the Aksu River, depending on WQI and to create WQI map based on GIS (4) to discuss the effects of the each water quality parameter on the WQI values. 2. Study area The Aksu River basin is located in the Antalya Basin, southwest Turkey, and the basin covers a total drainage area of approximately 3652 km2 (Fig. 1). The total length of the Aksu River is approximately 145 km, with headwaters Akdağ and is situated within Isparta Province and discharges to Mediterranean from the Antalya-Aksu border. The southern basin is narrower than the north. Two different climatic types, a Mediterranean climate and continental climate, are observed in the Aksu River basin. The north part of the basin has a Central Anatolia continental climate condition with hot, dry summers and cold, snowy winters. Most of the region usually has low precipitation throughout the year. A Mediterranean climate, is observed in the south part of the basin. The Mediterranean climate is characterized by dry summers and mild, moist winters. The altitude of the basin ranges between 2835 m and 0 m above sea level. The northwest and northeast mountain areas are highest areas, have lower temperatures, intense precipitation, and snow, whereas the south plain areas are generally warmer with


intense rainfall and evaporation. The average temperature of the basin is 14.4 °C according to meteorological station measurements. Rainfall data have been measured in Aksu meteorological station; annual average rainfall is 862.74 mm based on 19 years of Aksu meteorological station measurements. The maximum rainfall was 1235 mm in 1995. The average flow of the Aksu River is 94.98 hm3/year according to long-term flow observations (Güneş, 2014). The Kovada Lake and Karacaören-1 Dam are important surface waters in the study area. Lake Kovada is located in Lake District of the Turkey, situated in a Turkish National Park. The altitude of Kovada Lake is approximately 903 m above sea level. The shallow lake is 6– 7 m in depth, 9 km wide and 20.6 km long and covers an area of 8 km2. Lake Kovada is situated on allochthonous limestone and, groundwater flow and rainfall are important recharge sources to the lake. The Karacaören-1 Dam, located in the Middle Mediterranean basin was constructed for irrigation, flood prevention and energy production between 1977 and 1990. The plans for taking drinking and usage water from the Karacaören-1 Dam Lake are questionable due to pollution of drinking water sources and increased of demand. The Karacaören-1 Dam Lake is recharged mainly by the Aksu River and precipitation. 3. Hydrogeological setting The water chemistry and quality is related to both the lithology and the residence time of the water in contact with rock material (Varol and Davraz, 2014). Hence, hydrogeological properties of lithological units were investigated and aquifer units were determined using previous studies and field investigations. Thus, a hydrogeological map of the river basin was prepared using ArcGIS software based on characteristics of the lithological units (Fig. 3). Each lithological units observed in the basin has different hydrogeologic properties. According to hydrogeological assessments,

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the hydrogeology map of the river basin was prepared and lithologic units were grouped as porous permeable aquifer, karstic limestone aquifer, karstic travertine aquifer, semipermeable, slightly permeable, impermeable flysch and impermeable ophiolitic mélange. Alluvium was classified as porous permeable aquifer, limestone and travertine units were classified as karstic limestone aquifer and karstic travertine aquifer, respectively. Alluvium is the most important aquifer located in the Isparta and Ağlasun plains and east of Antalya city. The unit is composed of materials such as clay, silt, sand and gravel, unconformably covering all other lithological units. The other important aquifer units are Mesozoic carbonate units and travertine deposits in the study area, Antalya nappes control the boundary of these aquifers (Elhatip, 1997). Limestone is located in the bottom of the alluvium in each plain as indicated by well logs and fieldwork. Highly karstified limestones are observed in southern Turkey. The limestones were form a neritic and pelagic carbonate platform and are located in a wide area up to the Antalya gulf from Isparta. The unit is approximately 2500–3000 m thick and includes dolomitic limestone (Yalçınkaya et al., 1986). Limestones include many karstic features, such as sinkhole, cave, ponor and karstic springs, formed because of widespread karstification and active fracture systems (Davraz et al., 2009). In addition, the alluvium aquifer is recharged with springs discharging from allochthonous limestones and groundwater flow direction is towards the autochthonous karstic limestones (Davraz et al., 2009). Travertine covers an area of 630 km2 on the Antalya plain. Travertine formed as two plateaus on land. The elevation difference between these plateaus can reach to 100 m (Davraz et al., 2009). These travertines were formed from tufa, which is calcium carbonate precipitated from cool-water springs as described by Glover and Robertson (2003). The travertine units can be observed around Antalya city. While conglomerates were classified as semi permeable units due to karstic cement, volcanic units have slightly permeable properties.

Fig. 1. Location map.

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Fig. 2. The locations of the water samples and pollution sources.

Conglomerate is located around Sütçüler, north of the study area. Volcanic units are composed of tuff, andesite and dacite. Tertiary aged flysch units are composed of claystone, sandstone and conglomerate and located over wide areas in the river basin. Sandstone-shale-mudstone units and flysch were classified as impermeable flysch in the basin. In addition metamorphic rocks and ophiolitic units are classified as impermeable ophiolitic mélange (Fig. 3). The hydraulic conductivity of alluvium aquifer was between 8 × 10− 4 and 3 × 10− 2 m/s, and the hydraulic conductivity was

calculated as 2 × 10− 5 m/s for the karstic travertine aquifer (UNDP, 1983). The hydraulic conductivity has an important effect on groundwater movement and regional lithologic barriers control regional movement of large volumes of groundwater from higher recharge areas towards the plains. In the research area, according to the locations of lineaments, the locations of both fracture planes and karstic canals developed throughout its intersections permit N-S trending groundwater movement. The groundwater discharge to the karstic system is approximately 915 m in the northern part of the research area and 750 m for


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Fig. 3. Hydrogeological map.

travertine in the southern part. The groundwater flow direction in the limestones is from north to south according to the hydraulic conductivity of the research area (Davraz et al., 2009; UNDP, 1983). 4. Methodology 4.1. Station selection Twenty-one sampling stations were carefully selected to represent the main course and tributaries taking into consideration the criteria

for selecting the sampling stations given in the European Water Framework Directive (European Communities, 2000). Water Framework Directive (WFD 2000/60/EC) indicates that surveillance monitoring should be carried out in sufficient surface water bodies to provide an assessment of the overall surface water status within each catchment or sub-catchments within the river basin district. Additionally, sampling points should include major rivers as well as points at the downstream end of relevant sub-catchments. The main criteria in selecting these bodies are (1) the rate of water flow is significant within the river basin district as a whole; including points on large rivers where the catchment

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Fig. 4. The spatial distribution of pH (A), SO4 (B), NO2 (C) and NO2 (D) in the river.

area is N2500 km2, (2) the volume of water present is significant within the river basin district, including large lakes and reservoirs, and (3) at such other sites as are required to estimate the pollutant load which is transferred into the marine environment (European Communities, 2000). In the present study, the selection of sampling stations was

performed based on these criteria and also the river typology criteria determined by Ministry of Forestry and Water Management, Turkey. The river typology criteria are flow type, drainage area, precipitation, slope, altitude and geology. Seven different typologies were identified for the Aksu River and sampling sites represent each typology. Additionally,


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Fig. 5. The spatial distribution of COD (A), Mn (B), Pb (C) and Cr (D) in the river.

samples were collected especially from locations representing effects of punctual and diffuse source pollutants to the river basin. Previous studies that determined water quality in the Aksu River indicated that physicochemical properties of water samples taken monthly do not change significantly (Kalyoncu et al., 2005; Kalyoncu et al., 2008a; Kalyoncu et al., 2008b; Kalyoncu et al., 2009). Therefore, taking into account this literature review, monitoring was performed during

a rainy and dry period to observe clear changes in water quality and impacts of pollutants. A total of 42 water samples were collected from Aksu River in two periods (dry-October 2011 and wet-May 2012) to investigate river water quality. Geographical positions of sampling sites were measured with a portable GPS system. Samples were stored in two polyethylene bottles. One of the bottles was acidified with suprapure HCl for determining cations, and another was kept

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Fig. 6. Water Quality Index (WQI) map.

unacidified for the anion analyses. During sampling, bottles labelled to avoid misidentification were rinsed in clear spring water several times and then filled to the top to minimize the entrapment of air in water samples (Larsen et al., 2001), and stored at 4 °C in the refrigerator.

4.2. Analytical procedure Measurements of pH, temperature (T; °C), electrical conductivity (EC; μS/cm), and dissolved oxygen (DO; mg/L) were carried out in situ


with YSI Professional Plus handheld multiparameter instrument calibrated with standard solutions. The major cation and trace metal amounts were determined by inductively coupled plasma mass spectrometry (ICPMS) within group 2C-MS at the ACME Laboratory (Canada-ISO 9002 Accredited Co.). The titrimetric method was used for the determination of the hydroxyl, carbonate, and bicarbonate concentrations. Additionally, the argentometric method, based on titration of a sample with silver nitrate was used for the determination of chloride (AWWA, 1995). Spectrophotometer reagents and a WTW photoLabSpectral-12 Spectrophotometer were used for the determination of COD, phosphate, total phosphor, nitrite, nitrate and ammonia. The WTW Oxitop IS 6 Inductive Stirring System was used for the BOD. SO4 was determined spectrophotometrically using the barium sulfate turbidity method (Clesceri et al., 1998; AOAC, 1995). All analyses, except for major cation and trace metal concentrations, were performed at the laboratory of the Geothermal Energy, Groundwater, Mineral Resources Research Center of Suleyman Demirel University (Isparta, Turkey). The charge–balance error of the water samples was b5%, which is within the limits of acceptability. The data were analysed with the help of AquaChem Software. Water quality of the samples was assessed by calculating WQI values and using several guidelines (WHO, 2008; TSI-266, 2005).

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b50 Excellent water 50–100 Good water 100–200 Poor water 200–300 Very poor water N 300 Unsuitable for drinking In addition, to determine the water quality parameter with the greatest influence on WQI results, the effective weights of the each water quality parameter were calculated using the ArcGIS Spatial Analyst tool. The effective weight (Ewi) for each parameter was defined by dividing its subindex value by overall Water Quality Index value and the result multiplied by 100 as in the following equations:

Ewi ¼

SIi 100 WQI

ð5Þ

where, Ewi is effective weight of ith parameter; SIi is the subindex of ith parameter and WQI is the overall Water Quality Index computed by Eq. (4). The relative weights were compared with effective weights which is reflects the significance of each parameter with regard to the other parameters used in WQI calculations. 5. Results and discussions

4.3. Calculation of WQI WQI is defined as a rating of that reflects the composite influence of different water quality parameters (Sahu and Sikdar, 2008). Firstly, each of the chemical parameters was assigned different weights (wi) in a scale of 1 (least effect on water quality) to 5 (highest effect on water quality) based on their perceived effects on primary health and according to its relative importance in the drinking water quality (Table 3). The highest weight of 5 was assigned to parameters, that have critical health effects and whose presence above the critical concentration limits could limit the usability of the resource for domestic and drinking purposes (Yidana and Yidana, 2010; Varol and Davraz, 2015). NO3, NO2, Pb, Cr and Mn were assigned the highest weight (5) because of their major importance in water quality assessment; the minimum weight of 1 was assigned to parameters Ca, Mg and Na due to the least importance in water quality assessment. The relative weight (Wi) is computed from the following equation: n

Wi ¼ wi = ∑ wi i¼1

ð1Þ

where Wi is relative weight, wi is weight of each parameter and n is number of parameters. Then, a quality rating (qi) for each parameter is assigned by dividing its concentration in each water sample by its limits values given by the WHO (2008) and the result multiplied by 100: qi ¼

Ci 100 Si

ð2Þ

where qi is the quality rating, Ci is the concentration of each chemical parameter in each water sample in mg/L, and Si is the drinking water standard for each chemical parameter in milligrams per litre according to the guidelines of the WHO (2008). To calculate WQI, firstly SIi value should be determined with the following equations, SIi ¼ Wi x qi n

WQI ¼ ∑ SIi i¼1

ð3Þ ð4Þ

where, SIi is the subindex of ith parameter; qi is the quality rating based on concentration of ith parameter (Ramakrishnaiah et al., 2009). The computed WQI values are classified into five categories as follows (Sahu and Sikdar, 2008; Yidana and Yidana, 2010).

In general, water chemistry of rivers can reflect changes in watersheds, making rivers good indicators of land use (Meybeck and Helmer, 1989). Additionally, human activity and regional geology are two of the most important factors affecting hydrology and water quality of the rivers (Yang et al., 2012). Therefore, different water types and quality have been defined in rivers with long flow paths. Some studies have noted that diffuse pollution is a dominant factor contributing to the deterioration in water quality of rivers (Macleod and Whitfield, 1996; Chang and Wen, 1997; Kurunc et al., 2006; Avvannavar and Shrihari, 2008; Meeroff et al., 2008; Kannel et al., 2007; Branimarte et al., 2008; Li et al., 2009; Karakaya and Evrendilek, 2010; Alobaidy et al., 2010; Zhao et al., 2013). In this study, to determine water quality of the Aksu River, 42 water samples were collected from 21 locations selected in a wet and dry period (October-2011, May-2012). The analysis results of the physicochemical characteristics of the river waters are presented in Table 1 with a basic statistical summary. 5.1. Physicochemical parameters of the river water Water pH indicates acidic or basic nature and it is an important parameter in drinking and irrigation usages of waters. It has profound effects on water quality, affecting the solubility of metals, alkalinity and hardness of water (Osibanjo et al., 2011). The pH values of the river waters varied from 7.60 to 8.50 in wet and 8.21 to 9.18 during dry seasons. These results show that water samples of river have alkaline properties. In general, high pH values were determined in locations that are contact with carbonate rocks. Additionally, samples taken in the dry period have high pH values compared to the wet period. The water temperature values varied within a range 11.90–25.00 °C in the wet period and 12.97–27.25 °C in the dry period. The minimum temperature values were measured at the AK12 locations, maximum values were measured at the AK17 locations. Electrical conductivity (EC) of water is directly related to the concentration of dissolved solids in the water. In addition, contaminants can cause high EC values in surface waters. The EC values of groundwater vary within a range 430–1685 μS/cm in wet period and 460–1802 μS/cm in dry period. The maximum EC values were determined at locations AK3, AK6 and AK9, which appears to depend on increasing ion contents related to pollutants such as waste from a marble factory and sewage. In general, the large range of variation in pH, temperature and EC values of the water samples is notable. The main reason may be large differences in altitude because the Aksu River basin covers a large area from south to north; (the altitude of


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AK1 is 1085 m, the altitude of AK17 is 10 m.). Differences may also be due to other geographical characteristics, especially temperature. DO is the factor that determines biological changes by aerobic or anaerobic organisms. Thus, dissolved–oxygen measurement is vital for maintaining aerobic treatment processes intended to purify domestic and industrial wastewaters. The optimum value for good water quality is 4 to 6 mg/L of DO, which ensures healthy aquatic life in a water body (Alam et al., 2007; Avvannavar and Shrihari, 2008). The in situ measured dissolved oxygen (DO) values of the water samples ranging from 2.51 to 9.03 mg/L in the wet period, 2 and 7.44 mg/L in the dry period. Low dissolved oxygen values were measured at locations (AK3, AK9, and AK11) where pollutants were effective. Turbidity values of the water samples are between 0.64 and 35.67 JTU in the dry period. The permissible limit of the turbidity is 12.5 JTU according to TSI-266 (2005). The obtained results show that the turbidity values are over the limit values at the AK3, AK4, AK9 and AK10 locations. When comparing the wet period analysis results with dry period, higher concentrations were measured in the dry period for all major ions. CO3 concentrations vary from 0 to 23.33 mg/L and from 0 to 21.60 mg/L in dry and wet periods, respectively. HCO3 contents of water ranged from 23.58 to 695.78 mg/L in the dry period and from 22.04 to 650.26 mg/L in the wet period (Table 1). Bicarbonate is present in considerable amounts according to carbonate ions. Carbonate-rich rocks, such as crystalline limestone, dolomitic limestone, calcgranulite and kankar (lime-rich weathered mantle overlies carbonate rocks) are major sources for carbonate weathering. The available carbonates in these rocks might have been dissolved and added to the groundwater system during irrigation, rainfall infiltration and groundwater movement (Subramani et al., 2009). The concentration of SO4 varied in the range of 9.76–3307.57 mg/L in the dry period and 9.12–3091.19 mg/L in the wet period (Table 1). The main sulfate sources are atmospheric deposition, sulfate-bearing fertilizers and bacterial oxidation of sulfur compounds (Wayland et al., 2003; Sidle et al., 2000). Accordingly, the sulfate might come from the breakdown of organic substances of weathered soils, leachable sulfate from fertilizers and other human influences, such as sulfuric salts in domestic wastewater (Bahar and Yamamuro, 2008; Varol and Davraz, 2015). The Cl contents of water vary from 12.36 to 1198.16 mg/L and from 11.34 to 1099.26 mg/L in dry and wet periods, respectively (Table 1). The high values of chloride ions in water may result from

pollution by sewage waste and leaching of saline residues in the soil and/or may be attributed to anthropogenic activity (Chatterjee et al., 2010). The chloride and sulfate values do not meet the TSI-266 and WHO standards and are the major inorganic components deteriorating water quality for drinking water (Şener et al., 2013). The highest bicarbonate, chloride and sulfate values were measured at the AK3 and AK6 locations. At these locations, increases in anion contents were related to discharge of domestic and industrial wastewaters. Ca and Mg concentrations of water samples varied from 52.90 to 202.81 mg/L and 33.25 to 437.91 mg/L in the dry period, and 48.09 to 184.37 mg/L and 29.17 to 384.13 mg/L in the wet periods, respectively. The highest magnesium concentration was measured at location AK 17 and this value exceeded the TSI-266 limit. Na concentrations varied from 5.66 to 125.76 mg/L and from 5.10 to 113.30 mg/L in the dry and wet periods, respectively. K contents of water were determined as 1.53 to 33.57 mg/L and 1.40 to 30.8 mg/L in the dry and wet periods, respectively (Table 1). Ca+ and Mg2 + are the dominant cations in the river waters. Ca can be derived from dissolution of carbonate minerals (e.g., calcite, dolomite, aragonite) as well as carbonate cement within formations. The primary source of Mg in natural water is ferromagnesian minerals (olivine, diopside, biotite, hornblend) within igneous and metamorphic rocks and magnesium carbonate (dolomite) in sedimentary rock (Singh et al., 2012). The major source of Mg in groundwater is likely Mg-bearing minerals such as dolomite and magnesium sulfate minerals in the study area. The major ions of the water samples were evaluated using AquaChem 3.7 computer code. According to analysis results, the orders N CO−2 N Cl− and Ca+2 N Mg+2of anion and cations are HCO3 N SO−2 4 3 + + Na N K , respectively. However, some differences were found in order 2of ions depend on sampling locations. The order of anion is SO− 4 − − HCO3 N CO3 NCl at the AK3, AK6 and AK12 locations; the order of cation is Mg+2 N Ca+ 2 N Na+ N K+2 at the AK11, AK12, AK16 and AK17 locations. In general, the content of major ions changed according to water-rock interaction. However, the anthropogenic pollutants dominate in some locations. 5.2. Nutrients and trace metals of river water Nutrients such as the bioavailable forms of phosphorus and inorganic nitrogen (ammonia, nitrate and nitrite) are important factors

Table 1 Statistical summary of the physical and chemical parameters of the river water. Dry period

Wet period

Parameters

Minimum

Maximum

Mean

standard deviation

Minimum

Maximum

Mean

Standard deviation

EC (μS/cm) PH Temperature (°C) DO (mg/L) (mg/L) HCO−2 3 (mg/L) CO−2 3 − Cl (mg/L) (mg/L) SO−2 4 Na (mg/L) Ca (mg/L) K (mg/L) Mg (mg/L) Turbidity (JTU) COD (mg/L) NH+ 3 (mg/L) NH+ 4 (mg/L) NO− 3 (mg/L) NO− 2 (mg/L) TOC (mg/L) (mg/L) PO−3 4 Total phosphor (mg/L) Pb (mg/L) Cr (mg/L) Mn (mg/L)

460.10 8.21 12.97 2.01 23.58 0.00 12.36 9.76 5.66 52.90 1.53 33.25 0.64 15.14 0.03 0.03 1.53 0.04 19.12 0.02 0.06 0.0011 0.0011 0.0023

1802.95 9.18 27.25 7.44 695.78 23.33 1198.19 3307.57 125.76 202.81 33.57 437.91 35.67 178.09 4.12 4.63 6.42 1.71 955.58 4.42 3.55 0.0034 0.0192 0.1438

738.69 8.70 21.37 5.57 273.52 1.67 105.96 403.05 26.45 95.37 6.14 92.15 9.73 32.55 0.44 0.52 2.91 0.21 112.03 0.50 0.55 0.0013 0.0026 0.0268

331.47 0.24 3.74 1.23 123.42 5.16 275.45 903.20 32.74 32.71 8.10 83.63 10.00 37.91 1.07 1.29 1.22 0.45 263.40 1.12 1.00 0.0005 0.0043 0.0349

430.00 7.60 11.90 2.51 22.04 0.00 11.34 9.12 5.10 48.09 1.40 29.17 0.52 13.40 0.03 0.03 1.35 0.03 17.07 0.02 0.05 0.0010 0.0010 0.0020

1685.00 8.50 25.00 9.30 650.26 21.60 1099.26 3091.19 113.30 184.37 30.80 384.13 29.00 157.60 3.78 4.13 5.68 1.50 853.20 3.91 3.11 0.0030 0.0170 0.1250

690.37 8.06 19.61 6.96 255.63 1.54 97.21 376.68 23.83 86.70 5.63 80.83 7.91 28.81 0.40 0.46 2.58 0.18 100.03 0.44 0.49 0.0011 0.0023 0.0233

309.79 0.22 3.43 1.54 115.34 4.78 252.71 844.11 29.49 29.74 7.43 73.36 8.13 33.54 0.98 1.15 1.08 0.39 235.18 0.99 0.88 0.0005 0.0038 0.0303


affecting lake water quality (Şener et al., 2013). In addition they play an important role in the eutrophication process in surface waters (Soulsby et al., 2001). Orthophosphates can be quickly absorbed by plants and generally have a greater influence on eutrophication than nitrogen (Sharpley et al., 2001). Ammonia, on the other hand, is very toxic to fish when present in its un-ionized form (pH and temperature dependent), even at very low concentrations (Debels et al., 2005). Similar results were obtained for the nutrient and trace metal contents of water samples in the dry and wet periods. The ammonia (NH3) and ammonium (NH4) contents of the Aksu river water were determined as ranging from 0.03–4.12 mg/L and 0.03–4.63 mg/L in the dry period, and 0.03–3.78 mg/L and 0.03–4.13 mg/L in the wet period, respectively (Table 1). The NH4 concentrations measured at the AK3 and AK6 locations exceeded the permissible limit of Turkish Standards (TSI-266, 2005) 0.5 mg/L. The nitrite (NO2) and nitrate (NO3) contents of water were 0.04–1.71 mg/L and 1.53–6.42 mg/L in the dry period, and 0.03–1.50 mg/L and 1.15–5.68 mg/L in the wet period, respectively (Table 1). The NO3 contents of water samples are within the TSI-266 (2005) and WHO (2008) limits. But, the NO2 contents of water samples exceeded the permissible limit of TSI-266 (2005) at the AK3 and AK6 locations. In the dry period, the total organic carbon (TOC), phosphate and total phosphorous values of the water samples ranged from 19.12– 955.58 mg/L, 0.02–4.42 mg/L and 0.06–3.55 mg/L, respectively. The highest TOC, phosphate, and total phosphorous values were measured at AK3 and AK6. In addition, total phosphorous values were over the eutrophication control limit values (0.1 max) according to Water Pollution and Control Regulations in Turkey (Anonymous, 1998) at these locations. High levels of total phosphorus and other nutrients have been reported to encourage eutrophication which could further deplete the dissolved oxygen levels of the rivers (Fakayode, 2005; Minareci et al., 2009). Similar temporal variations in concentration of nutrients have been reported by Shrestha and Kazama (2007). Furthermore, the nutrient pollution in the branches of the Dahe River showed extremely high values especially in rainy season due to agricultural runoff (Tanaka et al., 2013). In this case study, the excessive nutrient values are related to pollutant sources such as sewage; and fertilizer and pesticide use by agricultural activities, according to field observations. COD tests predict oxygen requirements during the decomposition of organic matter and oxidation of inorganic chemicals. Theoretically, if COD concentration is higher, then the water is considered polluted (Amneera et al., 2013). The COD values were between 15.14 mg/L and 178.09 mg/L with all values from all sampling locations over the permissible limit set by WHO (2008) of 10 mg/L. River water had appreciable increases in COD values in the northern part of the river, likely from industrial activities. Trace metals may be present in natural surface water and groundwater, while the sources of these metals are associated with either natural processes or human activities (Al-Khashman, 2007). Pb, Al, Co, Cr, Cu, Ni, Zn, Fe, and Mn analysis were performed but only Pb, Cr and Mn concentrations were found above detection limits. The Pb, Cr and Mn contents of water samples were determined as ranging from 0.0011–0.0034 mg/L, 0.0011–0.0192 mg/L and 0.0023– 0.1438 mg/L in the dry period, respectively. The Pb, Cr and Mn contents were changed to 0.001–0.003 mg/L, 0.001–0.017 mg/L and 0.002–0.1250 mg/L in wet period, respectively (Table 1). The Pb and Cr concentrations are within the permissible limit of WHO (2008) and TSI 266 (2005). However, Mn concentrations exceeded the permissible limit of WHO (2008) and TSI 266 (2005) at the AK3, AK6 and AK12 locations. High values at the AK3 and AK6 locations are related to anthropogenic pollutants. However, the high Mn content at the AK12 is related to the water – rock interaction with ophiolitic rocks in the river basin. The high Mn concentrations might be related to high levels of manganese in the surrounding ore bearing landmass as the rivers flowing through the ore bearing terrain might be picking up the element (Zingde et al., 1976).

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5.3. Correlation analysis Pearson linear correlation matrix was generated using dry period analysis results using 13 parameters (pH, HCO3, Cl, SO4, Na, Ca, Mg, COD, NO3, NO2, Pb, Cr, Mn), the most effective water quality parameters to define any co-variation (Table 2). The obtained results indicates very strong positive correlations between Cl and SO4, Na, COD and Cr; SO4 with COD, NO2 and Cr; Na with COD, NO2; COD with NO2, Cr and strong positive correlations between HCO3 with Cl, SO4, Na, COD, NO2, Cr; Cl with NO2, SO4 with Mn; Na with Cr, Mn; and NO2 with Mn. pH shows weak and very weak positive correlations with all parameters except for Pb. While Ca has very weak positive correlation with HCO3, Na, NO3, NO2 and Mn; it has very weak negative correlations with Cl, SO4, COD, Pb and Cr. The similar weak negative correlation is shown between Mg and all parameters except for pH and Ca. NO3 shows a weak positive correlation with pH, HCO3, SO4, Na, NO2, Pb and Mn shows a moderate positive correlation with Cl and Cr. In general, correlation coefficients between pairs of water quality parameter concentrations indicate that chloride and sulfate values significantly correlate with pollutant parameters such as nutrients and trace metals. This suggests that these nutrients and trace metals have high values as a result of anthropogenic pollutants in the study area. 5.4. Assessment of the water quality using WQI In this study, water quality of the Aksu River was evaluated for drinking purposes. To assess water quality of the river, the WQI method was used. pH, HCO3, Cl, SO4, Na, Ca, Mg, COD, NO3, NO2, Pb, Cr and Mn were taken into account for calculation of the WQI value for each sampling location and both periods (dry and wet). The analysis results belonging to all 21 sampling points were used for quality evaluation. Furthermore, the World Health Organization (WHO, 2008) limits were utilized for calculations. The distribution maps of the water quality parameters (pH, HCO3, Cl, SO4, Na, Ca, Mg, COD, NO3, NO2, Pb, Cr and Mn) and also final WQI map of the river were prepared using Geographic Information System (GIS) techniques and presented in Figs. 4, 5 and 6. To calculate WQI values at each sampling point, the weight values were determined for each water quality parameter according to their relative importance in the overall quality of water for drinking purposes (Table 3). The highest weight of 5 was assigned to parameters such as nutrients and trace metals; which have the major effects on water quality especially for drinking purposes. Trace metal accumulations determined in waters indicate the presence of natural or anthropogenic sources. It may affect human health if they reach levels such that they constitute toxic pollutants (Yang et al., 2002; Bibi et al., 2007). Similarly, nitrogen compounds are present in water in the form of nitrate (NO3) and nitrite (NO2) ions. Nitrite is more toxic to animal and human health than nitrate (Varol and Davraz, 2015). Additionally, the consumption of water with high nitrate concentration causes blue babies or methemoglobinemia disease in infants, gastric carcinomas, abnormal pain, central nervous system birth defects, and diabetes (Vasanthavigar et al., 2010; Varol and Davraz, 2015). The parameters pH, SO4 and COD were assigned a weight of 4; HCO3 and Cl were assigned a weight of 3 taking into consideration their importance in water quality. The minimum weight of 2 was assigned to the Ca, Mg and Na because of their least importance in water quality. Then, the relative weights (Wi) were computed for each parameter using Eq. (1). The results are given in Table 3. WQI values were calculated using the other related Eqs. (2), (3) and (4) and water quality types were determined for each sampling point (Table 4). The computed WQI values are between 35.6133 and 337.5198 in the dry period; and between 32.1063 and 304.3386 in the wet period. In addition, the water quality of Aksu River is in the “excellent” to “unsuitable for drinking” range for both periods mainly due to input of municipal and industrial wastes and/or agricultural activities discharge at the bank of the river. In general, the same water quality classes were


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Table 2 Pearson's linear correlation matrix of physicochemical parameters. pH pH HCO3 Cl SO−2 4 Na Ca Mg COD NO3 NO2 Pb Cr Mn

1 0.242 0.229 0.287 0.250 0.003 0.314 0.256 0.300 0.308 −0.180 0.244 0.015

HCO3

Cl

SO−2 4

Na

1 0.856⁎⁎ 0.800⁎⁎ 0.838⁎⁎ 0.070 −0.067 0.834⁎⁎ 0.453⁎ 0.726⁎⁎

1 0.951⁎⁎ 0.897⁎⁎ −0.054 −0.080 0.995⁎⁎ 0.567⁎⁎ 0.873⁎⁎

1 0.937⁎⁎ −0.016 −0.073 0.966⁎⁎ 0.460⁎ 0.976⁎⁎

1 0.045 −0.095 0.918⁎⁎ 0.446⁎ 0.892⁎⁎

0.330 0.817⁎⁎ 0.294

0.365 0.982⁎⁎ 0.570⁎⁎

0.266 0.950⁎⁎ 0.735⁎⁎

0.241 0.880⁎⁎ 0.712⁎⁎

Ca

Mg

COD

NO3

NO2

Pb

Cr

Mn

1 0.736⁎⁎ −0.049 0.029 0.030 −0.196 −0.005 0.004

1 −0.074 −0.018 −0.027 −0.110 −0.057 −0.067

1 0.565⁎⁎ 0.895⁎⁎ 0.367 0.983⁎⁎ 0.626⁎⁎

1 0.316 0.116 0.565⁎⁎ 0.119

1 0.212 0.884⁎⁎ 0.770⁎⁎

1 0.345 0.156

1 0.581⁎⁎

1

⁎ p b 0.05. ⁎⁎ p b 0.01.

observed in all water samples except for at location AK17 for the dry and wet period (Table 4). The data points from locations AK2, AK4, AK5, AK7, AK8, AK14, AK15, AK16, AK19, AK20 and AK21 are classified as excellent water; the data points from locations AK1, AK9, AK10, AK11, AK12, AK13 and AK18 are classified as good water; while the data point of AK17 is classified as poor water in the dry period, and as good water in the wet period; the data point from locations AK6 is classified as very poor water and the data point from locations AK3 is classified as unsuitable for drinking purpose. The WQI distribution map shows that, while Aksu River has good water quality within Isparta, water quality of the river deteriorates seriously because of the discharging waste waters of Isparta city, leather industry and marble factories, located in the north of the basin (Fig. 6). If the river recharges from tributaries with high flow along the flow path, the quantity of impurities can be diluted and it becomes good water quality; again. The Aksu River is the main river recharging to the Karacaören-1 Dam Lake, which have drinking water taken from it for Antalya city. The water quality of the river is excellent after the Karacaören-1 Dam Lake along flow path. However, it again deteriorates and becomes poor due to intensive agricultural activity. The effective weight values of the each water quality parameter were determined by using ArcGIS Spatial Analyst tool and Eq. (5). The results were statistically summarized in Table 5 and the effective weights were compared with relative weights of the each water quality parameter. According to calculations, the highest mean effective weights value belongs to COD and Mg parameters with 32.81% and 24.19%, respectively and these parameters are the most effective parameters in the WQI calculations. However, the relative weight of COD (8.16%) is higher than the Mg parameter (4.08%). The other parameters (Ca and Na) with low relative weight also show low effective weight (Table 5). The pH also shows high effective weight (15.71%), exceeding

the relative weight assigned by WQI (8.16%). Cr and NO2 parameters have the lowest mean effective weights with 0.52% and 0.57%, respectively. The most striking result is that nutrients and trace metals have the highest relative weights, and at the same time have the lowest mean effective weights. This observation is primarily due to these parameters measured were at very low concentrations in water samples. 6. Conclusions In this study, water quality of the Aksu River and its suitability as drinking water were evaluated. The Aksu River is the main river recharging the Karacaören-1 Dam Lake. To evaluate water quality of the Aksu River, 21 sampling sites were determined and 24 water quality parameters were selected for seasonal monitoring and analysis. Physicochemical analysis results indicate that, the river water samples have alkaline properties, EC values of water samples varied in the range 460 and 1802 μS/cm for dry period. Due to negative effects of pollutants, three locations had measured low dissolved oxygen values and turbidity values over the permissible limit of TSI-266 (2005). The order of −2 − +2 N Mg+2 N Na+anion and cations are HCO3 N SO−2 4 N CO3 N Cl and Ca + K in water samples. In general, the major ion contents of water samples are related to water-rock interaction. The anthropogenic pollutants dominate some locations. The nutrient and trace metal values, except Table 4 WQI values and water types of the samples. Sample no 1 2 3

Table 3 Relative weight of chemical parameters. Parameters

WHO standards (2008)

Weight (wi)

Relative weight (Wi)

PH HCO3 Cl SO4 NO3 NO2 Ca Mg Na COD Pb Cr Mn

6.5–8.5 – 250 250 50 3 300 30 200 10.00 0.01 0.05 0.05

4 3 3 4 5 5 2 2 2 4 5 5 5 ∑wi = 49

0.0816 0.0612 0.0612 0.0816 0.1020 0.1020 0.0408 0.0408 0.0408 0.0816 0.1020 0.1020 0.1020 ∑Wi = 1

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

WQI

Water type

WQI

Dry period

Wet period

64.1574 45.3914

57.3307 40.6808

Good water Excellent water Unsuitable for 337.5198 drinking 39.1808 Excellent water 35.6133 Excellent water 255.5006 Very poor water 39.3942 Excellent water 43.5049 Excellent water 62.8933 Good water 65.8849 Good water 55.9677 Good water 69.5405 Good water 57.6326 Good water 49.1267 Excellent water 37.6103 Excellent water 47.7013 Excellent water 102.7453 Poor water 60.9263 Good water 42.6558 Excellent water 40.1331 Excellent water 43.9172 Excellent water

Water type

Good water Excellent water Unsuitable for 304.3387 drinking 35.0597 Excellent water 32.1063 Excellent water 231.2742 Very poor water 35.3570 Excellent water 39.0889 Excellent water 56.4539 Good water 58.9624 Good water 50.1829 Good water 61.7317 Good water 51.6043 Good water 45.8967 Excellent water 33.7350 Excellent water 42.8066 Excellent water 88.5460 Good water 54.5726 Good water 38.2708 Excellent water 36.0029 Excellent water 39.4048 Excellent water

Ş. Şener et al. / Science of the Total Environment 584–585 (2017) 131–144 Table 5 Statistical analysis of the effective weight. Parameters

Weight

Relative weight (Wi)

PH HCO3 Cl SO4 NO3 NO2 Ca Mg Na COD Pb Cr Mn

4 3 3 4 5 5 2 2 2 4 5 5 5

8.16 6.12 6.12 8.16 10.20 10.20 4.08 4.08 4.08 8.16 10.20 10.20 10.20

Effective weight (%) Min

Max

Mean

S.D.

2.52 1.40 0.56 0.81 0.18 0.18 0.34 2.90 0.29 17.02 0.46 0.23 1.19

22.51 6.93 8.69 37.12 2.02 2.28 5.05 59.73 2.11 44.73 3.27 1.44 13.38

15.71 4.13 1.57 8.88 1.01 0.57 2.41 24.19 0.67 32.81 2.19 0.52 5.35

5.38 1.42 1.34 7.87 0.49 0.44 0.82 14.80 0.39 6.71 0.74 0.22 4.01

for total phosphorous and nitrite, are within the permissible limit for drinking water set by WHO (2008) and TSI-266 (2005). In addition, the COD values in all the sampling points are over the WHO (2008) limit values. According to correlation analysis of the water quality parameters, chloride and sulfate values show no significant correlation with pollutant parameters, such as nutrients and trace metals. Water quality parameters pH, HCO3, Cl, SO4, Na, Ca, Mg, COD, NO3, NO2, Pb, Cr and Mn were used to calculate WQI values to evaluate river water quality. The computed WQI values are between 35.6133 and 337.5198 in the study. The WQI classification shows that the Karacaören-1 Dam Lake has good water status. The main pollutant sources of the Aksu River are wastewater discharging from Isparta city, leather industry, marble factories in the north of the Aksu river basin and agricultural activities in the south of the basin. Although, the water quality of the river is generally excellent, it is deteriorates due to anthropogenic origin such as local agricultural and industrial activities. The effects of water quality parameters on the WQI map were investigated, and the obtained results show that the highest mean effective weight value belong to the COD and Mg parameters compared with the other parameters. Consequently, environmental pollutants negatively affect the Aksu River water. Therefore, necessary protection measures should be taken as related to planned usage of the river water. References Abbasi, S.A., 2002. Water Quality Indices, State of the Art Report. Scientific Contribution Published by INCOH, National Institute of Hydrology, Roorkee, p. 73. Afsin, M., 1997. Hydrochemical evolution and water quality along the groundwater flowpath in the Sandikli Plain, Afyon Turkey. Environ. Geol. 31 (3–4), 221–230. Alam, J.B., Hossain, A., Khan, S.K., Banik, B.K., Islam, M.R., Muyen, Z., Rahman, M.H., 2007. Deterioration of water quality of Surma river. Environ. Monit. Assess. 134, 233–242. Alobaidy, A.H.M.J., Abid, H.S., Maulood, B.K., 2010. Application of water quality index for assessment of Dokan Lake ecosystem, Kurdistan region, Iraq. J. Water Resour. Prot. 2, 792–798. Al-Khashman, O.A., 2007. Assessment of the spring water quality in The Shoubak area, Jordan. Environmentalist 28, 203–215. Amneera, W.A., Najib, W.A.Z., Yusof, S.R.M., Ragunathan, S., 2013. Water quality index of Perlis River, Malaysia. Int. J. Civ. Environ. Eng. 13 (2), 1–6. Anonymous, 1998. Water pollution and control regulations in Turkey. Formal Gazette 199 (19), 13–74. Anonymous, 2015a. Antalya province environmental report. Environment and Urban Ministry, p. 170. Anonymous, 2015b. Isparta province environmental report. Environment and Urban Ministry, p. 112. AOAC, 1995. Association of official analytical chemists. 16 ed. Official methods of analysis AOAC International, Gaithersburg, MD March 1998 revision. Aston, S.R., Thornton, I., Webb, J.S., Purves, J.B., Milford, B.L., 1974. Stream sediment composition: an aid to water quality assessment. Water Air Soil Pollut. 3 (3), 321–325. Avvannavar, S.M., Shrihari, S., 2008. Evaluation of water quality index for drinking purposes for river Netravathi, Mangalore, South India. Environ. Monit. Assess. 143, 279–290. AWWA, 1995. Chemical oxygen demand, argentometric method. In standard methods for the examination of water and wastewater 4(49). American Public Health Association, Washington, pp. 5–12. Bahar, M.M., Yamamuro, M., 2008. Assessing the influence of watershed land use patterns on major ion chemistry of river waters in the Shimousa Upland Japan. Chem. Ecol. 24 (5), 341–355.

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