Eğirdir Lake basin was selected as the study area because the lake is the second largest freshwater lake in Turkey and groundwater in the basin is used as drinking water. In the present study, 29 groundwater samples were collected and analyzed for physico-chemical parameters to determine the hydrochemical characteristics, groundwater quality, and human health risk in the study area. The dominant ions are Ca2+, Mg2+, HCO32−, and SO42. According to Gibbs plot, the predominant samples fall in the rock–water interaction field. A groundwater quality index (WQI) reveals that the majority of the samples falls under good to excellent category of water, suggesting that the groundwater is suitable for drinking and other domestic uses. The Ca-Mg-HCO3, Ca-HCO3, Ca-SO4-HCO3, and Ca-Mg-HCO3-SO4 water types are the dominant water types depending on the water–rock interaction in the investigation area. Risk of metals to human health was then evaluated using hazard quotients (HQ) by ingestion and dermal pathways for adults and children. It was indicated that As with HQ ingestion >1 was the most important pollutant leading to non-carcinogenic concerns. It can be concluded that the highest contributors to chronic risks were As and Cr for both adults and children.

INTRODUCTION

Groundwater and surface waters are the major sources of water around the world. The economic development and ecological stability of many countries heavily depends on clean and adequate water. Groundwater is a valuable natural resource and is believed to be comparatively much cleaner and free from pollution than surface water (Armon & Kott 1994; Arya et al. 2012; Boateng et al. 2016). Also, due to a lack of surface water, groundwater has played a major role in meeting drinking and irrigation demands in arid and semi-arid regions (Khosravi et al. 2016). The need for groundwater is greater than ever before, related to industrial and agricultural developments (Kaviarasan et al. 2016). Groundwater quality is an important factor for drinking, domestic, industrial, and agricultural usages (Babiker et al. 2007). The quality of groundwater is affected by natural sources or numerous types of human activity (Kavaf & Nalbantçılar 2007; Nas & Berktay 2010; Varol & Davraz 2015a). Point and non-point pollution sources such as fertilizers, effluent run-off from industries, chemical dumping sites, and domestic sewage cause groundwater to become polluted and to create health problems (Rohul-Amin et al. 2012; Nalbantçılar & Pınarkara 2015). Therefore, systematic monitoring of the water quality parameters controlling hydrochemical processes are essential for sustainable usage of the water.

Hydrochemical studies of groundwater are providing a better understanding of possible changes in quality as development progresses (Varol & Davraz 2015a). A knowledge of hydrochemistry is important to assess the groundwater quality in any area in which groundwater is used for both irrigation and drinking needs (Srinivas et al. 2013). The conventional techniques such as trilinear plots, statistical techniques are widely accepted methods to determine the quality of water (Kumar et al. 2015). In addition, numerous water quality indices have been formulated all over the world, such as the 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) (Abbasi 2002; Debels et al. 2005; Kannel et al. 2007) to assess water quality. These indices are one of the most effective ways for water quality information provision to the public, concerned authorities, or policy-makers for water quality management and are considered to be one of the simplest methods used for overall water quality assessment (Shabbir & Ahmad 2015).

Groundwater contamination by inorganic and organic compounds of natural or anthropogenic origin represents a serious global environmental problem since groundwater is used as a significant drinking water source worldwide. The identification and characterization of associated human health risks are important problems that need to be addressed by environmental and medical geochemistry (Rapant & Krêmová 2007). It is known that water pollution may become a significant threat to human health (Davies 1983). Nearly 25,000 people die of such water pollution problems every day and one-third of urban inhabitants in developing countries cannot access safe drinking water (Yin & Deng 2006; Li & Ling 2006). Generally, drinking water containing different anions and heavy metals has significant adverse effects on human health either through deficiency or toxicity due to excessive intake (Varol & Davraz 2015b). For example, arsenic represents one of the most potentially toxic inorganic contaminants of groundwater worldwide (Focazio et al. 1999; Smith et al. 2000; Lin et al. 2002; Ahmed et al. 2004; Bundschuh et al. 2004; Tollestrup et al. 2005; Li et al. 2007; Rapant & Krêmová 2007; Kavcar et al. 2009; Phan et al. 2010). Fluoride, which generally occurs in nature, is beneficial to human health in trace amounts, but can be toxic in excess (Varol & Davraz 2015b). Risk assessment is the methodological approach in which the toxicity of a chemical is identified, characterized, and analyzed. Current knowledge of element and compound toxicity enables a descriptive (qualitative) risk assessment based on the identification of adverse effects (chronic, carcinogenic) and the quantitative assessment of risk level (calculation and map presentation of health risk) (Rapant & Krêmová 2007).

The geographic information system (GIS), a high performance computer-based tool, is playing a critical role in water resource management and pollution study. GIS represents a technological advancement in terms of overlay mapping techniques (Igboekwe & Akankpo 2011). Also, many studies have indicated that GIS is a powerful tool to assess water quality (Butler et al. 2002; Skubon 2005; Asadi et al. 2007; Babiker et al. 2007; Yammani 2007; Rangzan et al. 2008; Jeihouni et al. 2014; Krishnaraj et al. 2015; Varol & Davraz 2015a). GIS applications are used widely in groundwater studies, such as site suitability analyses, managing site inventory data, estimating vulnerability of groundwater to pollution potential from non-point sources of pollution, modeling groundwater movement, modeling solute transport and leaching, and integrating groundwater quality assessment models with spatial data to create spatial decision support systems (Engel & Navulur 1999). Additionally, mapping water quality indices within a GIS framework will be a useful tool for water quality management (Shabbir & Ahmad 2015).

The present study was carried out to evaluate the hydro-chemical characteristics, groundwater quality, and human health risk in Eğirdir Lake basin, which is one of the important drinking water basins in Turkey. In the study area, groundwater is used as drinking and irrigation water. There are several point and non-point pollution sources and groundwater quality is under threat in the region. Hence, this study has great importance for the identification of management options for the sustainable usages of the groundwater.

MATERIALS AND METHODOLOGY

Study area

The Eğirdir Lake catchment area is located within the Lake District in the southwest of Turkey (Figure 1). It is a sub-basin of the Antalya Basin which is one of the 26 major watersheds of Turkey. Eğirdir Lake is an indispensable water source for the region because it is the second largest freshwater lake with a 482 km2 surface area and available water capacity. The lake has different usages, such as irrigation, tourism, and fishing, and also for supplying the drinking water needs of Isparta city. The main recharging of the lake is rainfall, surface flow from streams, and underground flow from aquifers within the Eğirdir Lake basin.
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

There are two main groundwater basins, Uluborlu-Senirkent and Yalvaç-Gelendost, with a combined area of 525 km2. Yalvaç-Gelendost basin is located in the east of Eğirdir Lake and covers catchments of the Hoyran and Yalvaç rivers which are discharged to the lake. The most important settlements are Gelendost and Yalvaç, and there are many villages and municipalities connected to these districts. Senirkent-Uluborlu basin is located in the northwest of the lake and besides the Senirkent and Uluborlu districts, Büyükkabaca, Küçükkabaca, İleydağı, Yassıören, Ortayazı, Garip, Dereköy, Uluğbey, Başköy, and Gençali settlements are located in the basin. According to meteorological data obtained from the State Meteorology Works, the main rainfall varies from 348 to 788 mm in the study area (Şener & Davraz 2013). In recent years, surface and groundwater quality has been under threat in the basin due to many pollution sources, such as open dumps, fertilizer and pesticides practices in agricultural areas, uncontrolled sewerage system, mining activities, etc. (Şener 2010). Agricultural production takes place in approximately 1,147 km2 of the Eğirdir Lake basin. In these regions, natural and synthetic fertilizers and agricultural pesticides have been used in large amounts, and this is the main cause of the degradation of groundwater quality in the study area (Şener et al. 2009).

Geological and hydrogeological settings

The major factors that control the chemical quality of groundwater are anthropogenic and geologic impacts originating from pollution sources and water–rock interaction in the basin. Therefore, lithological characteristics of geological units were first identified and a geological map of the basin prepared (Figure 2). The oldest rock unit is the Upper Cambrian aged metamorphic, which consists of low grade meta-sedimentary material including meta-sandstone, meta-siltstone, schist, calc-schist, recrystallized limestone and quartzite as well as limestone, sandstone, and conglomerate affected by very low grade metamorphism in the study area (Demirkol 1982; Özgül et al. 1991). Mesozoic aged flysch overlies the metamorphic units unconformably. The flysch units at the southeast edge of the Eğirdir Lake consist of siliciclastic shales. Autochthonous carbonate rocks are located on a large scale in the east and west of the lake. In general, carbonate rocks consist of limestone and dolomite in the study area. Most of the carbonate rocks are highly karstified. Dolomite consists of white, massive, fine-grained dolomite with frequent supra-tidal textures about 150 m thick. Limestone is mainly in different colors, thin to medium thick bedded micritic limestones (Elitok 2012). Allochthonous ophiolitic complex overlies carbonate units unconformably. The units crop out in the south and northeast of the basin. The complex consists of radiolarian, cherts, diabase, sandstone, mudstone, and limestone. Paleogene aged clastics overlie the ophiolitic units in the west of the study area. Clastics consist of sandstone, claystone, siltstone, and conglomerates, namely flysch. Also, Neogene aged clastic and volcanic units are located in the basin. Clastic units crop out in the east of the basin, namely Neogene deposits. This fluviatile-lacustrine and terrestrial formations unconformably overlie the rock units that had previously existed. Although these units show an upward lithology of conglomerate–sandstone and siltstone–argillaceous limestones–limestone, they also show lateral and vertical transitions (Demirkol 1982). Volcanic units are located in the west of the lake and overlie carbonates and flysch unconformably. Quaternary deposits are composed of materials such as clay, silt, sand, and gravel, unconformably covering all of the other lithological units (Şener 2010).
Figure 2

Geological map of the lake basin.

Figure 2

Geological map of the lake basin.

Seven hydrogeologic units were determined in the study area based on hydrogeological properties of the lithological units. The Quaternary alluvium and the Mesozoic carbonate rocks are classified as aquifer units in the basin. Pyroclastic units and Neogene deposits were identified as semi-permeable units. In addition, ophiolite complex, flysch, and metamorphic rocks were identified as impermeable units due to having low permeability. Alluvium is the most important aquifer due to the porous structures and groundwater is taken from alluvial aquifer in the basin. According to groundwater level data, the groundwater depth varies from 3 to 36 m in the Senirkent-Uluborlu basin, 1–21.6 m in the Yalvaç basin, and 0.15–51.2 m in the Gelendost basin (Şener & Davraz 2013). The groundwater flow direction of the alluvium aquifer is toward the Eğirdir Lake in the basin. The amount of groundwater discharge to the east side of Eğirdir Lake by means of the karstic aquifer was determined as 114 hm3/year using the MODFLOW model (Soyaslan 2004). Yalvaç and Hoyran streams discharge 26.38 and 3.42 m3/s to Eğirdir Lake, respectively (Şener 2010). In the west of the lake, the amount of groundwater discharge from alluvium aquifer to Eğirdir Lake has been calculated as 7.8 hm3/year (Seyman 2005). Pupa Stream is the most important surface water flowing through the basin and it discharges 12.84 m3/year to Eğirdir Lake (Seyman 2005; Tay 2005). Hydraulic conductivity determined in the west of the study area varies between 8.72 × 10−6 and 2.24 × 10−4 m/s in alluvium. Hydraulic conductivity was calculated as 1.18 × 10−5 to 5.6 × 10−7 m/s in the east of the basin (Şener & Davraz 2013).

Pollution sources

Groundwater contamination can be classified into two groups: natural (geogenic) and man-made (anthropogenic) sources. Natural groundwater contamination is primarily caused by water–rock interaction, geothermal field effects and/or infiltration from low quality rivers, lakes, or seawater.

Anthropogenic groundwater contamination is generally attributed to the excessive use of: agricultural pesticides and fertilizers; domestic or industrial wastewaters, mining waste products; disposal of industrial waste; waste disposal sites; and poor well construction (Baba & Ayyıldız 2006; Şener et al. 2013). In the study basin, groundwater quality is affected negatively by point and non-point pollution sources such as domestic wastewater, constructed wetlands, uncontrolled landfills, industrial and agricultural activities (Figure 3). There are 81 settlements in the basin, and the total population of these settlements is approximately 175,000 (Güneş 2008). The Eğirdir and Yalvaç districts have wastewater treatment plants. However, the wastewater that originates from other settlements comes from scattered areas collecting in septic tanks or discharges to the streams directly. In such case, groundwater contamination is inevitable due to wastewater in the basin. There are nine constructed wetlands (in Senirkent, Uluborlu, Yalvaç, Gelendost, and Eğirdir districts) in the basin. Constructed wetlands are natural wastewater treatment systems which are used for accumulating domestic and industrial wastewaters. However, their capacity is not sufficient for treatment in the present case; on the contrary, they are affected by surface and groundwater quality negatively (Şener et al. 2013). In addition, the solid wastes are scattered in open dump areas because there is no controlled landfill site in the basin and the leachate generating from the open dump areas mix with surface or groundwater. In the basin, the main industrial pollutant is Yalvaç Leather Tanneries. The wastewater of the 62 enterprises belonging to the tanneries is purified in the Yalvaç treatment plant, but sometimes these wastewaters directly flow into the Yalvaç Stream and its drainage canal. The most important non-point pollution source is the agricultural activity observed in the basin, during which, fertilizers (synthetic and natural) and pesticides are used extensively in order to increase product quality and quantity. Farmyard manure is used more intensively than synthetic fertilizer with 95,000 t used annually. Also, annually used amounts of nitrogen (pure N), phosphorus oxide (P2O5), and potassium oxide (K2O) are 2,591, 2,002, and 343 t, respectively (Anonymous 1999; Şener et al. 2013).
Figure 3

Pollution sources map of the study area.

Figure 3

Pollution sources map of the study area.

Sampling and analytical procedure

Groundwater samples were collected during October 2014 representing the dry season in accordance with United States Environmental Protection Agency methodologies (USEPA 2000). A total of 29 water samples taken from wells/springs and the coordinates of these locations were loaded in the Magellan eXplorist 600 Manual Global Positioning System (GPS). Samples were stored in two polyethylene bottles. One of the bottles was acidified with suprapure HNO3 for determination of cations and another was kept unacidified for anion analyses. Bottles labeled 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), then stored at 4 °C in a refrigerator.

The pH, temperature (T, °C), electrical conductivity (EC, μS/cm), total dissolved solids (TDS, mg/L), and dissolved oxygen (DO, mg/L), were measured in situ with YSI Professional Plus handheld multi-parameter instruments that were calibrated with standard solutions. The major chemical constituents were analyzed at the ACME Laboratory (Canada-ISO 9002 Accredited Co.). The major cation (Ca, Mg, K, Na) and trace metal (Al, As, B, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Zn) amounts were determined by inductively coupled plasma mass spectrometry within group 2C-MS in the ACME Laboratory. Chloride, bicarbonate, sulfate, and nitrate analyses were performed in the Eğirdir Fisheries Research Institute Laboratory (Isparta/Turkey). The bicarbonate concentrations were determined by titrimetric method. The argentometric method based on titration of a sample with silver nitrate was used for the determination of chloride (AWWA 1995). Sulfate was determined spectrophotometrically by barium sulfate turbidity method (Clesceri et al. 1998; AOAC 1995). In addition, determination of nitrate was performed by using spectrophotometer reagents and WTW photoLab Spectral-12 Spektrophotometre. The calculated charge–balance error of the water samples is <5%, and this ratio is within the limits of acceptability.

Water quality index calculation

A water quality index (WQI) is a numeric expression used to evaluate the quality of water and to be easily understood by managers (Bordalo et al. 2006). The WQI provides comprehensible information from complex data of water quality for most domestic uses. Also, it can be used for evaluating the influence of natural and anthropogenic activities based on several key parameters of groundwater chemistry (Kumar et al. 2015). WQI is defined as a rating that reflects the composite influence of different water quality parameters (Sahu & Sikdar 2008). First, each of the chemical parameters was assigned a weight (wi) based on their perceived effects on primary health. The highest weight of five was assigned to parameters which have the major effects on water quality. The relative weight (Wi) is computed from the following equation: 
formula
1
where Wi is the relative weight, wi is the weight of each parameter and n is the number of parameters. ∑wi is the sum of all the parameters. Then, a quality rating scale (qi) for each parameter is assigned by dividing its concentration in each water sample by its respective standard and the result multiplied by 100: 
formula
2
where qi is the quality rating scale, Ci is the concentration of each chemical parameter in each water sample in mg/L, and Si is the World Health Organization standard for each chemical parameter in milligrams per liter according to the guidelines of the WHO (2008). To calculate WQI, first, SIi value should be determined with the following equations: 
formula
3
 
formula
4
The computed WQI values are classified into five categories as follows (Sahu & Sikdar 2008; Yidana & Yidana 2010).

 <50: excellent water

 50–100: good water

 100–200: poor water

 200–300: very poor water

 >300: unsuitable for drinking

Mapping

The advancement of GIS and spatial analysis helps to integrate the laboratory analysis data with the geographic data and to model the spatial distributions of water quality parameters, most robustly and accurately (Shabbir & Ahmad 2015). In the present study, the distribution maps of the water quality indices were prepared by using GIS techniques. First, the coordinates of the sampling points were determined using a hand-held GPS device and the locations were imported into GIS software through point layer. The database file including sample codes and all analyses results of the chemical parameters was prepared and this geodatabase was used to generate the spatial distribution map of the WQI. For this, ArcGIS software, Spatial Analyst extension, and inverse distance weight (IDW) interpolation methods were applied in the study. IDW is most suitable in the formulation of the interpolation of isodynamic contours. It also produces smooth and continuous surface changes between observations (Mantzafleri 2007).

RESULTS AND DISCUSSION

The qualities of a water resource depend on the management of anthropogenic discharges as well as the natural physicochemical characteristics of the catchment areas (Efe et al. 2005). The statistical summary of the physicochemical parameters and limit values for drinking waters is presented in Table 1. The chemical composition of the water samples (n = 29) in the study region shows a wide range. The temperature variation ranges from 10.16 to 20 °C with a mean value of 13.7 °C. pH is one of the most important operational water quality parameters, with the optimum pH required often being in the range of 6.5–9.5 (WHO 2004). It is controlled by carbon dioxide, carbonate, and bicarbonate equilibrium (Hem 1985). The minimum and maximum values of pH were measured as 8.2 and 8.68, respectively, with a mean value of 8.4. This shows that the groundwater of the study area is slightly alkaline due to the presence of carbonates and bicarbonates from karstic rocks. EC is a measure of water capacity to convey electric current. The presence of various dissolved salts is responsible for the EC of water. The EC in the study region varied from 175 to 692 μS/cm with an average of 425.90 μS/cm. TDS is a measure of the combined content of all inorganic and organic substances contained in a liquid in molecular, ionized, or microgranular suspended form (Saravanakumar & Ranjithkumar 2011). Concentrations of TDS in water vary considerably in different geological regions owing to differences in the solubilities of minerals (WHO 2004). TDS ranged from 222.2 to 794.9 mg/L with a mean value of 465.1 mg/L. The permissible limit of TDS of drinking water is 500 mg/L (WHO 2008). The observation shows that the TDS exceeded the maximum permissible limit in 12 locations. In addition, Todd (1980) suggested that groundwater be classified using TDS into very fresh (0–250 mg/L), fresh (250–1,000 mg/L), brackish (1,000–10,000 mg/L), and saline (10,000–100,000 mg/L) (Boateng et al. 2016). According to this categorization, all the groundwater samples fell under the fresh water type in the study area. DO is an essential parameter for the survival of all aquatic organisms and oxygen is the most well established indicator of water quality (Said et al. 2004). 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 & Shrihari 2008). The in situ measured DO values of the water samples ranged from 7.1 to 10.7 mg/L with a mean value of 8.9 mg/L.

Table 1

Statistical summary of the physical and chemical parameters of the groundwater

Parameters Minimum Maximum Mean Standard deviation WHO (2008)  TS-266 (2005)  
EC (μS/cm) 175.00 692.00 425.90 148.51   
pH 8.20 8.68 8.40 0.14 6.5–8.5 6.5–9.5 
Temperature (°C) 10.16 20.07 13.70 2.26   
DO (mg/L) 7.10 10.70 8.90 1.06   
TDS (mg/L) 222.2 794.9 465.1 176.9 500  
HCO32− (mg/L) 169.58 606.34 386.25 112.00 500  
Cl (mg/L) 8.87 26.95 13.88 4.50 250 250 
SO42− (mg/L) 7.11 495.50 138.67 159.31 250 250 
Na+ (mg/L) 1.20 25.76 10.76 6.21 200 200 
Ca2+ (mg/L) 43.86 198.21 108.31 46.28 300 200 
K+ (mg/L) 0.36 28.76 4.13 6.88  12 
Mg2+ (mg/L) 5.87 46.01 25.71 11.28 30 150 
NO3 (mg/L) 0.87 3.87 2.34 0.85 50  
Al (mg/L) 0.0010 0.4310 0.0441 0.1109 0.2 0.2 
As (mg/L) 0.0040 0.0128 0.0079 0.0023 0.01 0.01 
B (mg/L) 0.0090 0.0910 0.0422 0.0238   
Ba (mg/L) 0.0056 0.5560 0.1425 0.1327 0.7  
Cr (mg/L) 0.0009 0.0545 0.0102 0.0133 0.05 0.05 
Cu (mg/L) 0.0007 0.0341 0.0052 0.0086 
Fe (mg/L) 0.0090 0.5810 0.0468 0.1403  0.2 
Mn (mg/L) 0.00004 0.0485 0.0050 0.0121 0.4 0.05 
Ni (mg/L) 0.0001 0.0036 0.0006 0.0006 0.07 0.02 
Pb (mg/L) 0.0001 0.0035 0.0006 0.0011 0.01 0.01 
U (mg/L) 0.0003 0.0101 0.0030 0.0025 0.03  
Zn (mg/L) 0.0009 0.0741 0.0137 0.0198   
Parameters Minimum Maximum Mean Standard deviation WHO (2008)  TS-266 (2005)  
EC (μS/cm) 175.00 692.00 425.90 148.51   
pH 8.20 8.68 8.40 0.14 6.5–8.5 6.5–9.5 
Temperature (°C) 10.16 20.07 13.70 2.26   
DO (mg/L) 7.10 10.70 8.90 1.06   
TDS (mg/L) 222.2 794.9 465.1 176.9 500  
HCO32− (mg/L) 169.58 606.34 386.25 112.00 500  
Cl (mg/L) 8.87 26.95 13.88 4.50 250 250 
SO42− (mg/L) 7.11 495.50 138.67 159.31 250 250 
Na+ (mg/L) 1.20 25.76 10.76 6.21 200 200 
Ca2+ (mg/L) 43.86 198.21 108.31 46.28 300 200 
K+ (mg/L) 0.36 28.76 4.13 6.88  12 
Mg2+ (mg/L) 5.87 46.01 25.71 11.28 30 150 
NO3 (mg/L) 0.87 3.87 2.34 0.85 50  
Al (mg/L) 0.0010 0.4310 0.0441 0.1109 0.2 0.2 
As (mg/L) 0.0040 0.0128 0.0079 0.0023 0.01 0.01 
B (mg/L) 0.0090 0.0910 0.0422 0.0238   
Ba (mg/L) 0.0056 0.5560 0.1425 0.1327 0.7  
Cr (mg/L) 0.0009 0.0545 0.0102 0.0133 0.05 0.05 
Cu (mg/L) 0.0007 0.0341 0.0052 0.0086 
Fe (mg/L) 0.0090 0.5810 0.0468 0.1403  0.2 
Mn (mg/L) 0.00004 0.0485 0.0050 0.0121 0.4 0.05 
Ni (mg/L) 0.0001 0.0036 0.0006 0.0006 0.07 0.02 
Pb (mg/L) 0.0001 0.0035 0.0006 0.0011 0.01 0.01 
U (mg/L) 0.0003 0.0101 0.0030 0.0025 0.03  
Zn (mg/L) 0.0009 0.0741 0.0137 0.0198   

The major anions abundance in the study area was in the order of HCO32 > SO42 > Cl > NO3. The concentration of carbonates in natural waters is a function of dissolved carbon dioxide, temperature, pH, cations, and other dissolved salts. Bicarbonate is present in considerable amounts according to carbonate ions. Also, bicarbonate concentration of natural waters generally held within a moderate range by the effects of the carbonate equilibrium (Kumar et al. 2015). The bicarbonate concentration varied between 169.58 and 606.34 mg/L with a mean of 386.25 mg/L. The concentration of bicarbonate in the study area was mostly within the WHO (2008) standards except for four samples, Y20, Y21, Y22, and Y24. Sulfate occurs naturally in numerous minerals and is used commercially, principally in the chemical industry (Nas & Berktay 2010). It is one of the least toxic anions, even though dehydration is observed at high concentrations (Varol & Davraz 2015b). According to WHO (2008) and TS-266 (2005) the highest desirable and maximum permissible limit of sulfate is 250 mg/L. The sulfate concentration in the groundwater samples varied between 7.11 and 495.5 mg/L with a mean concentration of 138.67 mg/L. Sulfate exceeded the maximum permissible limit in six locations, Y21, Y23, Y24, Y27, Y28, and Y29.

The chloride in groundwater may be from diverse sources such as weathering, leaching of sedimentary rocks and soil, domestic and municipal effluents (SarathPrasanth et al. 2012). No health-based guideline value is proposed for chloride in drinking water. However, high concentrations of chloride give a salty taste to water and beverages (WHO 2004). The concentration of chloride ion in groundwater of the study area varied between 8.87 and 26.95 mg/L with a mean of 13.88 mg/L. All groundwater samples were within the maximum permissible limit of 250 mg/L. Nitrate is the product of nitrogenous material conversion and aerobic stabilization of organic nitrogen (Shabbir & Ahmad 2015). The nitrate concentration in groundwater and surface water is normally low but can reach high levels as a result of leaching or run-off from agricultural and/or contamination from human or animal wastes as a consequence of the oxidation of ammonia and similar sources (WHO 2004). The nitrate concentration ranged from 0.87 to 3.87 mg/L with a mean value of 2.34 mg/L in the study area; the permissible limit of nitrate is given as 50 mg/L by the TS-266 and WHO for drinking water. According to analysis results, all of the groundwater samples were within the maximum permissible limit.

The order of the major cation trend was Ca2+ > Mg2+ > Na+ > K+ and Ca2+ is the dominant ion among the cations in the study area. Calcium and magnesium are directly related to hardness of the water and these ions are the most abundant elements in the surface and groundwater, and exist mainly as bicarbonates and, to a lesser degree, in the form of sulfate and chloride (Kumar et al. 2015). 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 Ca and Mg concentrations of water samples vary from 43.86 to 198.21 mg/L and 5.87 to 46.01 mg/L, respectively. Calcium concentrations of all groundwater samples were within the maximum permissible limit of 300 mg/L. However, magnesium exceeded the maximum permissible limit in ten locations, Y3, Y4, Y8, Y9, Y11, Y12, Y19, Y20, Y21, and Y26. The Na concentration ranged from 1.2 to 25.76 mg/L with a mean value of 10.76 mg/L in the study area; the permissible limit of sodium is given as 200 mg/L by the TSE and WHO for drinking water. According to analysis results, groundwater samples were within the maximum permissible limit. Potassium is a naturally occurring element and the amount of potassium varied between 0.36 and 28.76 mg/L with an average value of 4.13 mg/L. It was found that all the samples with potassium content within the permissible limit, except for sample Y14.

Trace metals are the most persistent and dangerous pollutants in aquatic ecosystems. They disturb the natural balance of ecosystems that have been formed evolutionarily over a long period of time (Arnason & Fletcher 2003; Bai et al. 2011). Trace metal accumulations determined in waters and sediment indicate the presence of natural (geogenic) or anthropogenic sources. Water–rock interaction within drainage basins is the primary source for the lithogenic contribution of heavy metals into an aquatic system. Also, anthropogenic sources such as urban and industrial waste water, mining and smelting operations, combustion of fossil fuels, processing and manufacturing industries, waste disposal including dumping are primary pollutants for aquatic systems (Pardo et al. 1990; Şener et al. 2014). Al, As, B, Ba, Cr, Cu, Fe, Mn, Ni, Pb, U, and Zn analyses were performed in water samples and it was found that the concentrations of the Ba, Cu, Mn, Ni, Pb and U parameters were within the permissible limit given by the TSE and WHO for drinking water. However, there is no limit value for B and Zn parameters. The minimum, maximum, and mean values of the trace metal parameters can be seen in Table 1. The Al and As contents of water samples were determined as a range 0.001–0.431 mg/L and 0.004–0.0128 mg/L, respectively. According to analysis results, all of the groundwater samples were within the maximum permissible limit in terms of Al except for sample Y8. However, As concentrations were over the permissible limit of the WHO (2008) and TS-266 (2005) in six locations, Y1, Y4, Y5, Y6, Y7, and Y8. The B and Ba contents of water samples were determined as a range 0.009–0.0910 mg/L and 0.005–0.556 mg/L, respectively and all of the groundwater samples were within the maximum permissible limit for Ba. The concentration of Cr ranges from 0.0009 to 0.054 mg/L in groundwater and was mostly within the TSE and WHO standards except for one sample (Y3). The concentration of Cu was within the maximum permissible limits with minimum and maximum of 0.0007 and 0.0341 mg/L, respectively.

The Fe concentration in the groundwater samples varied between 0.009 and 0.581 mg/L with an average concentration of 0.0468 mg/L, and the concentration of Fe was within the WHO standards except for only one water sample (Y8). The trace metal results indicated that sample Y8 has high Al, As, and Fe content and sample Y3 has high Cr content. Both of them are located in the Senirkent-Uluborlu basin and the main reason for this excessive dosage is pesticide and fertilizer usage during agricultural activities and also water–rock interaction with volcanic units. The Mn and Ni contents of water samples were determined as a range 0.00004–0.0485 mg/L, and 0.0001–0.0036 mg/L, respectively. In addition, the Pb, U, and Zn contents of water samples were determined as a range 0.0001–0.0035 mg/L, 0.0003–0.0101 mg/L, and 0.0009–0.0741 mg/L, respectively (Table 1).

Hydrochemical types

Water chemistry is mainly influenced by water–rock interaction taking place from the recharge area to sampling location (Purushothaman et al. 2014). In addition, hydrogeochemical types reflect the effects of chemical reactions occurring between the minerals within the lithologic framework and groundwater (Varol & Davraz 2015a). In the present study, the groundwater samples were classified hydrochemically using major cations and anions with conventional Piper trilinear diagram (Piper 1944) to determine the similarities between groundwater in the basin. Also, a Gibbs diagram was used to understand the genesis of groundwater.

Gibbs diagram

Gibbs (1970) suggested that a simple plot of TDS versus the weight ratio of Na+/(Na+ + Ca2+) could provide information on the relative importance of three major natural mechanisms controlling surface water chemistry: (1) atmospheric precipitation; (2) rock weathering; and (3) evaporation and fractional crystallization. Three distinct fields, rainfall dominance, evaporation dominance, and rock–water interaction dominance areas are shown in the Gibbs diagram, also known as Boomerang plot, which is widely employed to assess functional sources of dissolved chemical constituents (Gibbs 1970). The Na+, Ca2+, and TDS data of the groundwater samples have been plotted in the diagram and Gibbs plot shows that the all sampling points mostly fall in the rock–water interaction dominance zone (Figure 4).
Figure 4

Piper diagram showing the groundwater type.

Figure 4

Piper diagram showing the groundwater type.

Piper trilinear diagram

The graphical representation of Piper's diagram (Piper 1944) is extensively used to assess the geochemical evolution in groundwater flow systems. Two triangular fields were plotted separately from the percentage meq/L values of the cations Ca2+ and Mg2+ (alkaline earths) and Na+ (alkali), and the anions HCO3 (weak acid) and SO42− and Cl (strong acid) and then projected onto the central field for the representation of overall characteristics of water. This plot reveals similarities and differences among groundwater samples and also relationships for large sample groups (Srivastava & Ramanathan 2008). The Piper trilinear diagram was prepared using Aquachem 3.7 scientific software and results indicate that the Ca-Mg-HCO3, Ca-HCO3, Ca-SO4-HCO3, and Ca-Mg-HCO3-SO4 dominant water types were observed in the study area due to water–rock interaction (Figure 5). The limestone and dolomitic rocks are located on a large scale in the study area and carbonate-rich rocks such as crystalline limestone, dolomitic limestone are the major sources for carbonate weathering. The sulfate might come from the breakdown of organic substances of weathered soils, leachable sulfate from fertilizers and other human influences like sulfuric salts in domestic wastewater (Bahar & Yamamuro 2008; Varol & Davraz 2015a). The elevated SO4 concentrations were related to weathered soils and also pollutants such as fertilizer and pesticide used in agricultural activities according to field observations. Ca can be derived from the dissolution of carbonate minerals (e.g., calcite, dolomite, aragonite) as well as carbonate cement within formations. In addition, the major source of Mg in groundwater is probably Mg-bearing minerals such as dolomite and magnesium sulfate minerals in the study area.
Figure 5

Gibbs diagram.

Figure 5

Gibbs diagram.

WQI

WQI is an important parameter for identifying the water quality and its sustainability for drinking purposes (Magesh et al. 2013; Kumar et al. 2015). In the present study, WQI was computed for 29 groundwater samples. First, important water quality parameters (pH, TDS, HCO32−, Cl, SO42, NO3, Ca2+, Mg2+, Na+, As, Cr, Mn, Pb, Ni, and U) were selected and a weight was assigned to each parameter depending upon the effect on human health. In addition, the limit values of the World Health Organization's guidelines (WHO 2008) were utilized in the calculations (Table 2). The highest weight of 5 was assigned to parameters such as TDS, NO3, and As which have the major effects on water quality for especially drinking purposes. HCO32, Ca2+, Mg2+, Pb, Ni, and U have been assigned a weight of 3 taking into consideration their importance in water quality. The relative weights (Wi) are computed for each parameter and results are given in Table 2. The WQI values have been calculated using related equations (Equations (2)–(4)) given in the Methodology section, and WQI results and water types for individual samples are presented in Table 3. WQI interpolation maps of the study area were prepared using GIS techniques and are presented in Figure 6.
Table 2

Relative weight of chemical parameters

Parameters WHO (2008) standards Weight (wiRelative weight (Wi
pH 6.5–8.5 0.0727 
TDS 500 0.0909 
HCO3 500 0.0545 
Cl 250 0.0727 
SO4 250 0.0727 
NO3 50 0.0909 
Ca 300 0.0545 
Mg 30 0.0545 
Na 200 0.0364 
As 0.01 0.0909 
Cr 0.05 0.0727 
Mn 0.40 0.0727 
Pb 0.01 0.0545 
Ni 0.07 0.0545 
0.03 0.0545 
  ∑wi = 55 ∑Wi = 1 
Parameters WHO (2008) standards Weight (wiRelative weight (Wi
pH 6.5–8.5 0.0727 
TDS 500 0.0909 
HCO3 500 0.0545 
Cl 250 0.0727 
SO4 250 0.0727 
NO3 50 0.0909 
Ca 300 0.0545 
Mg 30 0.0545 
Na 200 0.0364 
As 0.01 0.0909 
Cr 0.05 0.0727 
Mn 0.40 0.0727 
Pb 0.01 0.0545 
Ni 0.07 0.0545 
0.03 0.0545 
  ∑wi = 55 ∑Wi = 1 
Table 3

WQI values and water types of the samples

Sample no. WQI Water type 
31.8 Excellent water 
21.7 Excellent water 
37.8 Excellent water 
39.1 Excellent water 
30.3 Excellent water 
33.3 Excellent water 
30.0 Excellent water 
37.8 Excellent water 
26.4 Excellent water 
10 26.1 Excellent water 
11 32.9 Excellent water 
12 26.3 Excellent water 
13 36.0 Excellent water 
14 28.7 Excellent water 
15 24.7 Excellent water 
16 28.3 Excellent water 
17 26.3 Excellent water 
18 26.3 Excellent water 
19 34.2 Excellent water 
20 41.1 Excellent water 
21 50.8 Good water 
22 38.7 Excellent water 
23 48.8 Excellent water 
24 54.6 Good water 
25 31.1 Excellent water 
26 32.4 Excellent water 
27 45.5 Excellent water 
28 50.9 Good water 
29 43.7 Excellent water 
Sample no. WQI Water type 
31.8 Excellent water 
21.7 Excellent water 
37.8 Excellent water 
39.1 Excellent water 
30.3 Excellent water 
33.3 Excellent water 
30.0 Excellent water 
37.8 Excellent water 
26.4 Excellent water 
10 26.1 Excellent water 
11 32.9 Excellent water 
12 26.3 Excellent water 
13 36.0 Excellent water 
14 28.7 Excellent water 
15 24.7 Excellent water 
16 28.3 Excellent water 
17 26.3 Excellent water 
18 26.3 Excellent water 
19 34.2 Excellent water 
20 41.1 Excellent water 
21 50.8 Good water 
22 38.7 Excellent water 
23 48.8 Excellent water 
24 54.6 Good water 
25 31.1 Excellent water 
26 32.4 Excellent water 
27 45.5 Excellent water 
28 50.9 Good water 
29 43.7 Excellent water 
Figure 6

Spatial distribution map of the WQI.

Figure 6

Spatial distribution map of the WQI.

The computed WQI for the groundwater samples values ranged from 21.7 to 54.6 in the study and the groundwater quality of the study area is in the ‘excellent’ to ‘good’ range. 89.66% of the groundwater samples represented ‘excellent water’ and 10.34% of the samples fell into ‘good water’ category. According to the WQI distribution map of the study area, the samples taken from the Yalvaç-Gelendost basin have lower WQI values compared with samples taken from the west of the lake. Domestic, agricultural, and/or industrial pollutants are the main reason for the poor water quality in the Yalvaç-Gelendost basin.

Risk assessment on human health

A human health risk assessment is the process to estimate the nature and probability of adverse health effects in humans who may be exposed to chemicals in contaminated environmental media, now or in the future. On a global scale, pathogenic contamination of drinking water poses the most significant health risk to humans. However, significant risks to human health may also result from exposure to non-pathogenic, toxic contaminants that are often ubiquitous in water. In this section, the hazard index (HI) method was used to assess the overall potential for non-carcinogenic effects of metals and nitrate contaminants in groundwater in the Eğirdir Lake basin. To assess the overall potential for non-carcinogenic effects posed by more than one chemical, an HI approach is developed based on USEPA (1986a) Guidelines for Health Risk Assessment of Chemical Mixtures.

Hazard identification is to estimate the hazardous effect of the contaminant. In the first step, the hazard level is examined by physical and chemical properties of contaminants such as mobility and contaminant levels at the point of exposure where the contaminants are exposed to the environment. The second step is exposure assessment, estimated by average daily dose (ADD) using the identification of intensity, frequency, exposure period, and pathway of contaminants. In the third step, dose–response assessment examines the relationship between adverse effects and exposure levels of carcinogenic and non-carcinogenic chemicals. The two principal toxicity indices are known as SF (cancer slope factor) and reference dose (RfD). The SF and RfD values can be obtained from the EPA Integrated Risk Information System (IRIS) on-line database and EPA Health Effects Assessment Summary Tables (USEPA 1994). Risk characterization is the final step that predicts the level of risk. The results of exposure assessment and dose–response assessment are integrated to derive quantitative estimates of cancer risk and HI (USEPA 1986a, 1986b, 1989; Lee et al. 2006).

Exposure of human beings to metals could occur via three main pathways, direct ingestion, inhalation through mouth and nose, and dermal absorption through exposed skin, while ingestion and dermal absorption are common for drinking water (USEPA 2004; De Miguel et al. 2007; Wu et al. 2009; Li & Zhang 2010). The dose received through the individual pathway considered was determined using Equations (5) and (6) modified from the US Environmental Protection Agency (USEPA 2004). 
formula
5
 
formula
6
where Ci is the concentration of pollutant i in drinking water (mg/L); L is the daily water ingestion rate (L/day); EF is the exposure frequency (days/year); ED is the exposure duration (year), taken as 30 years for non-carcinogens and 70 years for carcinogens; BW is the bodyweight (kg); AT is the average exposure time (in day), 30 years × 365 days/year for non-carcinogens and 70 years × 365 days/year for carcinogens; SA exposed skin area, unit in cm2; Kp dermal permeability coefficient in water, unit in cm/h; ET exposure time, unit in h/day.

The default values used to estimate potential exposure from drinking contaminated water and the default values (USEPA 2001) that are used to estimate dermal ADD for adults and children are given in Table 4. Kp is dermal permeability coefficient in water, unit in cm/h. The default permeability constants for all other inorganic compounds are provided in USEPA (2004) and Kp values for several inorganic compounds are given in Table 5.

Table 4

Default values for drinking water and dermal routine

Variables Adults Children 
L (L/day) 
EF (days/year) 365 oral; 350 dermal 365 oral; 350 dermal 
ED (year) 30 
BW (kg) 70 15 
AT (in day) 10,950 2,190 
SA (cm218,000 6,600 
ET (h/day) 2.6 
Variables Adults Children 
L (L/day) 
EF (days/year) 365 oral; 350 dermal 365 oral; 350 dermal 
ED (year) 30 
BW (kg) 70 15 
AT (in day) 10,950 2,190 
SA (cm218,000 6,600 
ET (h/day) 2.6 
Table 5

Kp, RfD, SF values and HI for each element

  Kp (cm/h) RfD-oral (mg/kg/d) RfD-dermal (mg/kg/d) SF (kg d/mg) HQingestion HQdermal HI = ΣHQs Cancer riskingestion 
Adult Child Adult Child Adult Child Adult Child 
Al 1 × 10−3 0.1  1.23 × 10−2/8.57 × 10−5 1.00 × 10−2/6.67 × 10−5 2.76 × 10−3/6.41 × 10−6 1.82 × 10−3/8.44 × 10−6 1.51 × 10−2/7.00 × 10−5 1.06 × 10−2/7.09 × 10−5   
As 1 × 10−3 3 × 10−4 1.23 × 10−4 1.5 1.22 × 100/3.81 × 10−1 2.84 × 100/8.89 × 10−1 2.08 × 10−2/6.67 × 10−2 1.37 × 10−2/4.08 × 10−2 1.29 × 100/4.02 × 10−1 2.89 × 100/9.03 × 10−1 1.71 × 10−4/5.49 × 10−4 1.01 × 10−3/9.90 × 10−4 
1 × 10−3 0.2 1.4 × 10−2  1.01 × 10−2/9.71 × 10−3 1.00 × 10−2/7.33 × 10−3 1.01 × 10−3/9.62 × 10−4 1.08 × 10−3/9.95 × 10−4 1.00 × 10−2/9.43 × 10−4 1.09 × 10−2/8.00 × 10−3   
Ba 1 × 10−3 0.2 1.4 × 10−2  1.16 × 10−2/8.06 × 10−4 1.85 × 10−1/6.4 × 10−3 1.16 × 10−2/8.79 × 10−4 1.68 × 10−2/5.78 × 10−4 1.05 × 10−1/9.84 × 10−3 2.02 × 10−1/6.97 × 10−3   
Cr 1 × 10−3 3 × 10−3 7.5 × 10−5 7.3 × 10−3 1.33 × 10−2/8.57 × 10−3 1.21 × 100/8.00 × 10−2 1.20 × 10−2/8.80 × 10−2 1.02 × 10−1/9.56 × 10−3 1.08 × 10−1/9.40 × 10−2 1.00 × 10−1/9.19 × 10−2 1.14 × 10−5/7.51 × 10−7 1.30 × 10−5/8.27 × 10−7 
Cu 1 × 10−3 3.7 × 10−2 8 × 10−3  1.38 × 10−2/9.03 × 10−3 1.17 × 10−2/8.83 × 10−3 1.04 × 10−4/7.21 × 10−5 1.80 × 10−3/8.44 × 10−5 1.29 × 10−2/9.97 × 10−3 1.21 × 10−2/9.09 × 10−3   
Fe 1 × 10−3 0.3 0.14  5.53 × 10−2/8.57 × 10−4 1.29 × 10−1/5.56 × 10−3 2.66 × 10−3/9.16 × 10−5 1.75 × 10−3/7.53 × 10−5 5.80 × 10−2/8.98 × 10−4 1.31 × 10−1/5.63 × 10−3   
Mn 1 × 10−3 0.14 1.84 × 10−3  1.23 × 10−3/8.16 × 10−6 1.04 × 10−2/5.24 × 10−5 1.69 × 10−2/8.01 × 10−5 1.11 × 10−2/9.17 × 10−6 1.21 × 10−2/6.08 × 10−5 1.02 × 10−3/7.76 × 10−5   
Ni 2 × 10−4 2 × 10−2 5.4 × 10−3  1.00 × 10−3/1.43 × 10−4 1.07 × 10−2/3.33 × 10−4 1.66 × 10−5/2.37 × 10−6 1.09 × 10−5/1.56 × 10−6 1.02 × 10−3/1.45 × 10−4 1.07 × 10−2/3.35 × 10−4   
Pb 1 × 10−4 3.6 × 10−3  5.5 × 10−2 2.78 × 10−2/7.94 × 10−4 1.11 × 10−2/7.41 × 10−3     1.26 × 10−6/9.43 × 10−7 1.28 × 10−5/7.33 × 10−7 
1 × 10−3 3 × 10−3  0.4 1.30 × 10−2/9.14 × 10−3 1.22 × 10−1/9.91 × 10−2     1.15 × 10−4/9.26 × 10−6 1.02 × 10−4/6.67 × 10−6 
Zn 6 × 10−4 0.3 6 × 10−2  1.07 × 10−3/8.57 × 10−5 1.09 × 10−3/9.78 × 10−4 1.63 × 10−4/8.98 × 10−6 1.78 × 10−4/7.17 × 10−6 1.14 × 10−3/9.15 × 10−5 1.68 × 10−2/9.96 × 10−4   
NO3  1.6 0.8  6.52 × 10−2/1.55 × 10−2 1.01 × 10−1/9.67 × 10−2 1.01 × 10−3/7.93 × 10−4 1.10 × 10−3/8.81 × 10−4 1.62 × 10−2/6.81 × 10−2 1.02 × 10−1/9.83 × 10−2   
  Kp (cm/h) RfD-oral (mg/kg/d) RfD-dermal (mg/kg/d) SF (kg d/mg) HQingestion HQdermal HI = ΣHQs Cancer riskingestion 
Adult Child Adult Child Adult Child Adult Child 
Al 1 × 10−3 0.1  1.23 × 10−2/8.57 × 10−5 1.00 × 10−2/6.67 × 10−5 2.76 × 10−3/6.41 × 10−6 1.82 × 10−3/8.44 × 10−6 1.51 × 10−2/7.00 × 10−5 1.06 × 10−2/7.09 × 10−5   
As 1 × 10−3 3 × 10−4 1.23 × 10−4 1.5 1.22 × 100/3.81 × 10−1 2.84 × 100/8.89 × 10−1 2.08 × 10−2/6.67 × 10−2 1.37 × 10−2/4.08 × 10−2 1.29 × 100/4.02 × 10−1 2.89 × 100/9.03 × 10−1 1.71 × 10−4/5.49 × 10−4 1.01 × 10−3/9.90 × 10−4 
1 × 10−3 0.2 1.4 × 10−2  1.01 × 10−2/9.71 × 10−3 1.00 × 10−2/7.33 × 10−3 1.01 × 10−3/9.62 × 10−4 1.08 × 10−3/9.95 × 10−4 1.00 × 10−2/9.43 × 10−4 1.09 × 10−2/8.00 × 10−3   
Ba 1 × 10−3 0.2 1.4 × 10−2  1.16 × 10−2/8.06 × 10−4 1.85 × 10−1/6.4 × 10−3 1.16 × 10−2/8.79 × 10−4 1.68 × 10−2/5.78 × 10−4 1.05 × 10−1/9.84 × 10−3 2.02 × 10−1/6.97 × 10−3   
Cr 1 × 10−3 3 × 10−3 7.5 × 10−5 7.3 × 10−3 1.33 × 10−2/8.57 × 10−3 1.21 × 100/8.00 × 10−2 1.20 × 10−2/8.80 × 10−2 1.02 × 10−1/9.56 × 10−3 1.08 × 10−1/9.40 × 10−2 1.00 × 10−1/9.19 × 10−2 1.14 × 10−5/7.51 × 10−7 1.30 × 10−5/8.27 × 10−7 
Cu 1 × 10−3 3.7 × 10−2 8 × 10−3  1.38 × 10−2/9.03 × 10−3 1.17 × 10−2/8.83 × 10−3 1.04 × 10−4/7.21 × 10−5 1.80 × 10−3/8.44 × 10−5 1.29 × 10−2/9.97 × 10−3 1.21 × 10−2/9.09 × 10−3   
Fe 1 × 10−3 0.3 0.14  5.53 × 10−2/8.57 × 10−4 1.29 × 10−1/5.56 × 10−3 2.66 × 10−3/9.16 × 10−5 1.75 × 10−3/7.53 × 10−5 5.80 × 10−2/8.98 × 10−4 1.31 × 10−1/5.63 × 10−3   
Mn 1 × 10−3 0.14 1.84 × 10−3  1.23 × 10−3/8.16 × 10−6 1.04 × 10−2/5.24 × 10−5 1.69 × 10−2/8.01 × 10−5 1.11 × 10−2/9.17 × 10−6 1.21 × 10−2/6.08 × 10−5 1.02 × 10−3/7.76 × 10−5   
Ni 2 × 10−4 2 × 10−2 5.4 × 10−3  1.00 × 10−3/1.43 × 10−4 1.07 × 10−2/3.33 × 10−4 1.66 × 10−5/2.37 × 10−6 1.09 × 10−5/1.56 × 10−6 1.02 × 10−3/1.45 × 10−4 1.07 × 10−2/3.35 × 10−4   
Pb 1 × 10−4 3.6 × 10−3  5.5 × 10−2 2.78 × 10−2/7.94 × 10−4 1.11 × 10−2/7.41 × 10−3     1.26 × 10−6/9.43 × 10−7 1.28 × 10−5/7.33 × 10−7 
1 × 10−3 3 × 10−3  0.4 1.30 × 10−2/9.14 × 10−3 1.22 × 10−1/9.91 × 10−2     1.15 × 10−4/9.26 × 10−6 1.02 × 10−4/6.67 × 10−6 
Zn 6 × 10−4 0.3 6 × 10−2  1.07 × 10−3/8.57 × 10−5 1.09 × 10−3/9.78 × 10−4 1.63 × 10−4/8.98 × 10−6 1.78 × 10−4/7.17 × 10−6 1.14 × 10−3/9.15 × 10−5 1.68 × 10−2/9.96 × 10−4   
NO3  1.6 0.8  6.52 × 10−2/1.55 × 10−2 1.01 × 10−1/9.67 × 10−2 1.01 × 10−3/7.93 × 10−4 1.10 × 10−3/8.81 × 10−4 1.62 × 10−2/6.81 × 10−2 1.02 × 10−1/9.83 × 10−2   

Risk characterization was quantified by carcinogenic risk and non-carcinogenic risk. Potential non-carcinogenic risks, reflected by the hazard quotient (HQ), were estimated by comparing exposure or average intake of contaminants from each exposure route (ingestion, dermal) with the corresponding RfD using Equations (7) and (8). If the HQ exceeds 1 (HQ> 1), there might be concern about non-carcinogenic effects. The higher the value the greater likelihood of adverse non-carcinogenic health effect (USEPA 1989; Khan et al. 2008; Muhammad et al. 2011; Qaiyum et al. 2011; Jamaludin et al. 2013; Equation (3)). To evaluate the total potential non-carcinogenic risks posed by more than one pathway, the HI was introduced, which was the sum of the HQs from all applicable pathways. HI > 1 indicated the potential for an adverse effect on human health or the necessity for further study (USEPA 2001, 2004): 
formula
7
where RfD is the reference dose (mg/kg/d), and given in Table 5. RfD values employed in this study were obtained from USEPA (IRIS 2005; Kavcar et al. 2009; Li & Zhang 2010).
Carcinogenic risk is the probability of an individual developing any type of cancer from lifetime exposure to carcinogenic hazards. The acceptable or tolerable risk for regulatory purposes is in the range of 10−6 to 10−4 (Rodriguez-Proteau & Grant 2005; Lim et al. 2008; Li & Zhang 2010). The following is the basic equation used for lifetime cancer risk assessment of pollutant in drinking water: 
formula
8
where cancer risk is the carcinogenic risk of pollutant in drinking water (unitless); SF is the slope factor for pollutant (kg d/mg); ADD is the chronic daily intake (mg/kg d) for pollutant (Li et al. 2007; Liu et al. 2009). A SF is most commonly used to evaluate potential human carcinogenic risks (USEPA 1989). SF values (Table 5) are provided in the IRIS on the website of USEPA (2013).
Table 5 presents the HQ, HI, and cancer risk values for the oral and dermal pathways relating to adults and children, respectively. HQingestion (hazard index by oral ingestion) of all trace elements and nitrate except As for adults was less than 1 in all sample locations, suggesting that these elements posed little hazard. HQingestion of As was more than 1 implying that As may cause adverse health effects and potential non-carcinogenic concerns. HQingestion of As for adults was more than 1 in three locations and was near unity in other locations (Figure 7). However, HQingestion of As for child was more than 1 in all of the locations except for two samples in the Eğirdir Lake basin, indicating serious health concerns (Figure 8). In addition, HQingestion of Cr for child was more than 1 at only one location. The HQdermal (hazard index by dermal absorption) of all trace elements and nitrate for adults and children was below unity, indicating that these metals posed little hazard via dermal absorption. The largest value of HQdermal was 0.15, which was for Cr for child. Overall HI of As for child exceeded 1, and HI of As for adults also exceeded 1 in seven locations (Y1, Y4, Y5, Y6, Y7, Y8, Y14). HI of Cr for child was more than 1 in Y3 location and HI of Cr for adults was near 1 in the same location. It can be concluded that the highest contributors to chronic risks were As and Cr for both adults and children. This indicated that As posed serious health concerns to the local residents via oral intake, while other metals via oral intake and all the elements via dermal absorption had no or little health threat.
Figure 7

Spatial distribution map of HQingestion for As (adults).

Figure 7

Spatial distribution map of HQingestion for As (adults).

Figure 8

Spatial distribution map of HQingestion for As (children).

Figure 8

Spatial distribution map of HQingestion for As (children).

Carcinogenic risk of As through oral intake for child exceeded the target risk of 1 × 10−4 (Table 5) and indicated that the ingestion of water over a long lifetime could increase the probability of cancer. The risk assessment indicated that As was the most important pollutant in the Eğirdir Lake basin. Previous studies reported adverse health effects including hypertension, neuropathy, diabetes, skin lesions, and cardiovascular diseases through high arsenic intake (Avani & Rao 2007; Bhattacharya et al. 2007; Wu et al. 2009). Therefore, special attention should be paid to arsenic for local residents, particularly for sensitive children, and measures need to be taken to sustain a healthy aquatic ecosystem.

CONCLUSIONS

The hydrochemical characteristics, groundwater quality, and human health risk in Eğirdir Lake basin was evaluated in the present study. Eğirdir Lake is an indispensable water source for the region due to usage aims such as drinking/irrigation water, tourism, and fishing. The groundwater is used as drinking and irrigation water in the study area. A total of 29 samples were taken from wells within the study area and analyzed for hydrochemical and quality evaluation. The order of anion and cations are HCO32− > SO42− > Cl > NO3 and Ca2+ > Mg2+ > Na+ > K+ in groundwater samples and HCO3 and Ca2+ are the dominant ions among the anions and cations in the study area. According to Piper trilinear diagram, Ca-Mg-HCO3, Ca-HCO3, Ca-SO4-HCO3, and Ca-Mg-HCO3-SO4 are the dominant water types related to water–rock interaction. Carbonate weathering plays an active role in development of the water type. Also, Gibbs plot indicates that all the samples fall in the rock–water interaction dominance zone. The results of the analyses were compared with drinking water limit values determined by WHO (2008) and TS-266 (2005) to assess the potability of groundwater. The results show that HCO32, SO4−2, Mg2+, Al, As, Cr, and Fe are a little over the WHO (2008) and TS-266 (2005) limit values. All the other parameters are within the permissible limit for drinking water. In the study area, groundwater quality is slowly reaching an unsuitable stage for drinking water due to industrial and agricultural activities. Moreover, water–rock interaction affects the water quality adversely. According to the WQI classification, the water samples fall into the excellent to good water category. In general, groundwater quality in Yalvaç-Gelendost basin is lower than Senirkent-Uluborlu basin. However, high Al, As, and Fe content was determined in Senirkent-Uluborlu basin related to water–rock interaction and agricultural activities.

Risk assessment is an attempt to identify and quantify potential risks to human health resulting from exposure to various contaminants. In this study, oral ingestion and dermal route were taken into consideration for adults and children. HQingestion of As for adults was more than 1 in three locations. However, HQingestion of As for children was more than 1 in all of the locations except for two samples in the Eğirdir Lake basin, indicating serious health concerns. In addition, HQingestion of Cr for child was more than 1 at only one location. It can be concluded that the highest contributors to chronic risks were As and Cr for both adults and children. This indicated that As posed serious health concerns for local residents via oral intake, while other metals via oral intake and all the elements via dermal absorption posed no or little health threat.

REFERENCES

REFERENCES
Abbasi
S. A.
2002
Water Quality Indices, State of the Art Report
.
Scientific contribution no. INCOH/SAR-25/2002
,
National Institute of Hydrology, INCOH
,
Roorkee
,
India
, p.
73
.
Ahmed
K. M.
Bhattacharya
P.
Hasan
M. A.
Akhter
S. H.
MahbubAlam
S. M.
Hossain Bhuyian
M. A.
Badrul Imam
M.
Khan
A. A.
Sracek
O.
2004
Arsenic enrichment in groundwater of the alluvial aquifers in Bangladesh: an overview
.
Applied Geochemistry
19
,
181
200
.
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
.
Environmental Monitoring and Assessment
134
,
233
242
.
Anonymous
1999
Project of Protection Eğirdir Lake as a Source of Drinking Water, Final Report
.
Hacettepe University, Application and Research Center for Environment
, p.
156
.
AOAC
1995
Official Methods of Analysis
,
16 edn
.
Association of official analytical chemists
,
Gaithersburg, MD
,
USA
,
March 1998 revision
.
Armon
R.
Kott
P.
1994
The health dimension of groundwater contamination
. In:
Groundwater Contamination and Control
(
Zoller
U.
, ed.).
Marcel Dekker
,
New York
,
USA
.
Arya
S.
Kumar
V.
Sharma
S.
2012
Analysis of water quality parameters of groundwater in and around Diamond Cement Industry, Jhansi, Central India
.
International Journal of Current Research
4
(
3
),
75
77
.
Asadi
S. S.
Vuppala
P.
Reddy
M. A.
2007
Remote sensing and GIS techniques for evaluation of groundwater quality in Municipal Corporation of Hyderabad (Zone-V), India
.
International Journal of Environmental Research and Public Health
4
(
1
),
45
52
.
AWWA
1995
Chemical oxygen demand, argentometric method
. In:
Standard Methods for the Examination of Water and Wastewater
,
19th edn
.
American Public Health Association
,
Washington, DC
,
USA
, pp.
5
12
.
Baba
A.
Ayyıldız
Ö.
2006
Urban groundwater pollution in Turkey
. In:
Urban Groundwater Management and Sustainability
(
Tellam
J. H.
Rivett
M. O.
Israfilov
R. G.
Herringshaw
L. G.
, eds).
Springer, The Netherlands
, pp.
93
110
.
Babiker
I. S.
Mohamed
A. M.
Hiyama
T.
2007
Assessing groundwater quality using GIS
.
Water Resources Management
21
(
4
),
699
715
.
Bhattacharya
P.
Welch
A. H.
Stollenwerk
K. G.
McLaughlin
M. J.
Bundschuh
J.
Panaullah
G.
2007
Arsenic in the environment: biology and chemistry
.
Science of the Total Environment
379
,
109
120
.
Brown
R. M.
McCleeland
N. I.
Deininger
R. A.
Tozer
R. G.
1970
A water quality index – do we care?
Water and Sewage Works
117
,
339
343
.
Bundschuh
J.
Farias
B.
Martin
R.
Storniolo
A.
Bhattacharya
P.
Cortes
J.
Bonorino
G.
Albouy
R.
2004
Groundwater arsenic in the Chaco-Pampean plain, Argentina: case study from Robles county, Santiago del Estero province
.
Applied Geochemistry
19
,
231
243
.
Butler
M.
Wallace
J.
Lowe
M.
2002
Groundwater Quality Classification using GIS Contouring Methods for Cedar Valley, Iron County, Utah
.
In: Digital mapping techniques, Workshop Proceedings, US Geological Survey Open-File Report 02-370
.
Clesceri
L. S.
Greenberg
A. E.
Eaton
A. D.
1998
Standard Methods for the Examination of Water and Wastewater
,
20th edn
.
American Public Health Association (APHA), American Water Works Association (AWWA) and Water Environment Federation (WEF)
,
Washington, DC
,
USA
.
Davies
B. E.
1983
Heavy metal contamination from base metal mining and smelting: implications for man and his environment
. In:
Applied Environmental Geochemistry
(
Thornton
I.
, ed.).
Academic Press
,
London
,
UK
, pp.
425
462
.
Demirkol
C.
1982
The stratigraphy around Yalvaç-Akşehir and comparison with West Taurus
.
Geology Engineering Journal
14
,
3
14
.
Efe
S. I.
Ogban
F. E.
Horsfall
M.
Jr
Akporhonor
E. E.
2005
Seasonal variations of physico-chemical characteristics in water resources quality in western Nigeria delta region, Nigeria
.
Journal of Applied Sciences and Environmental Management
9
(
1
),
191
195
.
Engel
B. A.
Navulur
K. C. S.
1999
The role of geographical information systems in groundwater engineering
. In:
The Handbook of Groundwater Engineering
(
Delleur
J. W.
, ed.).
CRC Press
,
Boca Raton, FL
,
USA
, pp.
703
718
.
Focazio
M. J.
Welch
A. H.
Watkins
S. A.
Helsel
D. R.
Horn
M. A.
1999
A Retrospective Analysis on the Occurrence of Arsenic in Groundwater Resources of the United States and Limitations in Drinking-Water Supply Characterizations
.
Water-Resources Investigations Report, United States Geological Survey, 21
.
Hem
J. D.
1985
Study and interpretation of the chemical characteristics of natural water: US Geological Survey Water-Supply Paper 2254, 3rd edn, 263
.
Igboekwe
M. U.
Akankpo
A. O.
2011
Application of geographic information system (GIS) in mapping groundwater quality in Uyo, Nigeria
.
International Journal of Geosciences
2
,
394
397
.
IRIS (Integrated Risk Information System)
2005
US Environmental Protection Agency
,
Cincinnati, OH
. .
Jamaludin
N.
Sham
S. M.
Ismail
S. N. S.
2013
Health risk assessment of nitrate exposure in well water of residents in intensive agriculture area
.
American Journal of Applied Sciences
10
(
5
),
442
448
.
Jeihouni
M.
Toomanian
A.
Shahabi
M.
Alavipanah
S. K.
2014
Groundwater quality assessment for drinking purposes using GIS modeling (Case study: City of Tabriz)
. In:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W3, 2014 The 1st ISPRS International Conference on Geospatial Information Research
,
15–17 November 2014
,
Tehran, Iran
.
Kannel
P. R.
Lee
S.
Lee
Y. S.
Kanel
S. R.
Khan
S. P.
2007
Application of water quality indices and dissolved oxygen as indicators for river water classification and urban impact assessment
.
Environmental and Monitoring Assessment
132
,
93
110
.
Kavaf
N.
Nalbantçılar
M. T.
2007
Assessment of contamination characteristics in waters of the Kütahya Plain, Turkey
.
CLEAN – Soil Air Water
35
(
6
),
585
593
.
Kavcar
P.
Sofuoglu
A.
Sofuoglu
S.
2009
A health risk assessment for exposure to trace metals via drinking water ingestion pathway
.
International Journal of Hygiene and Environmental Health
212
,
216
227
.
Kaviarasan
M.
Geetha
P.
Soman
K. P.
2016
GIS-based ground water quality monitoring in Thiruvannamalai District, Tamil Nadu, India
. In:
Proceedings of the International Conference on Soft Computing Systems in Advances in Intelligent Systems and Computing,
397
, pp.
685
700
.
Khan
A. A.
Paterson
R.
Khan
H.
2003
Modification and application of the CCME WQI for the communication of drinking water quality data in newfoundland and Labrador
. In:
Presented at 38th Central Symposium on Water Quality Research, Canadian Association on Water Quality
,
10–11 February 2003
,
Burlington, Canada
.
Khosravi
H.
Karimi
K.
Nakhaee Nezhad Fard
S.
Mesbahzadeh
T.
2016
Investigation of spatial structure of groundwater quality using geostatistical approach in Mehran Plain, Iran
.
Pollution
2
(
1
),
57
65
.
Krishnaraj
S.
Kumar
S.
Elango
K. P.
2015
Spatial analysis of groundwater quality using geographic information system – a case study
.
IOSR Journal of Environmental Science, Toxicology and Food Technology
9
(
2
),
1
6
.
Lee
S. W.
Lee
B. T.
Kim
J. Y.
Kim
K. W.
Lee
J. S.
2006
Human risk assessment for heavy metals and as contamination in the abandoned metal mine areas, Korea
.
Environmental and Monitoring Assessment
119
(
1–3
),
233
244
.
Li
E. X.
Ling
B.
2006
Effect of water pollution on human health
.
Sanitary Engineering of China
5
(
1
),
3
5
.
Li
J.
Huang
G. H.
Zeng
G.
Maqsood
I.
Huang
Y.
Lia
J.
2007
An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination
.
Journal of Environmental Management
82
,
173
188
.
Liu
Y.
Zheng
B.
Fu
Q.
Meng
W.
Wang
Y.
2009
Risk assessment and management of arsenic in source water in China
.
Journal of Hazardous Materials
170
,
729
734
.
Magesh
N. S.
Krishnakumar
S.
Chandrasekar
N.
Soundranayagam
J. P.
2013
Groundwater quality assessment using WQI and GIStechniques, Dindigul district, Tamil Nadu, India
.
Arabian Journal of Geosciences
6
(
11
),
4179
4189
.
Mantzafleri
N.
2007
Geographic Simulation of the Water Quality of Lake Kastoria
.
Postgraduate Dissertation
,
Department of Agriculture Ichthyology and Aquatic Environment, University of Thessaly
,
Greece
(in Greek)
.
Nalbantçılar
M. T.
Pınarkara
S. Y.
2015
Impact of industry on groundwater contamination: a case study in Konya city, Turkey
.
GlobalNEST International Journal
17
(
4
),
796
815
.
Nas
B.
Berktay
A.
2010
Groundwater quality mapping in urban groundwater using GIS
.
Environmental and Monitoring Assessment
160
(
1
),
215
227
.
Özgül
N.
Bölükbaşı
S.
Alkan
H.
Öztaş
Y.
Korucu
M.
1991
Tectono-stratigraphic units of the Lakes district, western Taurides
. In:
Proceedings of the Ozan Sungurlu Symposium
, pp.
213
237
.
Phan
K.
Sthiannopkao
S.
Kim
K. W.
Wong
M. H.
Sao
V.
Hashim
J. H.
Yasin
M. S. M.
Aljunid
S. M.
2010
Health risk assessment of inorganic arsenic intake of Cambodia residents through groundwater drinking pathway
.
Water Research
44
,
5777
5788
.
Piper
A. M.
1944
A graphic procedure in the chemical interpretation of water analysis
.
American Geophysical Union Transactions
25
,
914
923
.
Purushothaman
P.
Rao
M. S.
Rawat
Y. S.
Kumar
C. P.
Krishan
G.
Parveen
T.
2014
Evaluation of hydrogeochemistry and water quality in Bist-Doabregion, Punjab, India
.
Environmental and Earth Sciences
72
(
3
),
693
706
.
Qaiyum
M. S.
Shaharudin
M. S.
Syazwan
A. I.
Muhaimin
A.
2011
Health risk assessment after exposure to aluminium in drinking water between two different villages
.
Journal of Water Resource and Protection
3
,
268
274
.
Rangzan
K.
Charchi
A.
Abshirini
E.
Dinger
J.
2008
Remote sensing and GIS approach for water-well site selection, Southwest Iran
.
Environmental and Engineering Geoscience
14
(
4
),
315
326
.
Rodriguez-Proteau
R.
Grant
R. L.
2005
Toxicity evaluation and human health risk assessment of surface and ground water contaminated by recycled hazardous waste materials
. In:
Handbook of Environmental Chemistry
(
Kassim
T. A.
, ed.),
Vol. 5, Part F, Vol. 2.
Springer-Verlag
,
Berlin
, pp.
133
189
.
Rohul-Amin
S. S.
Anwar
Z.
Khattak
J. Z. K.
2012
Microbial analysis of drinking water and water distribution system in New Urban Peshawar
.
Current Research Journal of Biological Sciences
4
(
6
),
731
737
.
Saravanakumar
K.
Ranjithkumar
R.
2011
Analysis of water quality parameters of groundwater near Ambattur industrial area, Tamilnadu, India
.
Indian Journal of Science and Technology
4
(
5
),
660
662
.
Şener
Ş.
2010
Hydrogeochemical Investigation of Eğirdir Lake Water and Bottom Sediments
.
PhD Thesis
,
Süleyman Demirel University
,
Turkey
(in Turkish)
.
Şener
Ş.
Davraz
A.
Karagüzel
R.
2013
Evaluating the anthropogenic and geologic impacts on water quality of the Eğirdir Lake, Turkey
.
Environmental Earth Sciences
70
,
2527
2544
.
Şener
Ş.
Davraz
A.
Karagüzel
R.
2014
Assessment of trace metal contents in water and bottom sediments from Eğirdir Lake, Turkey
.
Environmental Earth Sciences
71
(
6
),
2807
2819
.
Seyman
F.
2005
Hydrogeological Investigations of Senirkent-Uluborlu (Isparta) Basin
.
Master Thesis
,
Süleyman Demirel University
,
Isparta
,
Turkey
, p.
96
(in Turkish)
.
Singh
A. K.
Mahato
M. K.
Neogi
B.
Tewary
B. K.
Sinha
A.
2012
Environmental geochemistry and quality assessment of mine water of Jharia coalfield, India
.
Environmental Geology
65
,
49
65
.
Skubon
B. A.
Jr
2005
Groundwater quality and GIS investigation of a shallow sand aquifer, Oak opening region, North West Ohio
.
Geological Society of America. Abstracts Programs
37
(
5
),
94
.
Smith
A. H.
Lingas
E. O.
Rahman
M.
2000
Contamination of drinking water by arsenic in Bangladesh: a public health emergency
.
Bulletin of the World Health Organization
78
,
1093
1103
.
Soyaslan
I. I.
2004
Hydrogeological Investigations of Eastern Egirdir Lake and Groundwater Modeling
.
PhD Thesis
,
Suleyman Demirel University
,
Turkey
(in Turkish)
.
Tay
Ş.
2005
Geological and Hydrogeological Investigations Senirkent-Uluborlu (Isparta) Basin Related to Secure Landfilling Location Election
.
Master Thesis
,
Süleyman Demirel University
,
Isparta
,
Turkey
, p.
89
(in Turkish)
.
Todd
D. K.
1980
Groundwater Hydrology
.
Wiley
,
New York
,
USA
.
Tollestrup
K.
Frost
F. J.
Cristiani
M.
McMillan
G. P.
Calderon
R. L.
Padilla
R. S.
2005
Arsenic induced skin conditions identified in southwest dermatology practises: an epidemiologic tool?
Environmental Geochemistry and Health
27
,
47
53
.
TS-266
2005
Standards for Drinking Waters-266
.
Turkish Standards Institution
,
Ankara
,
Turkey
.
USEPA
1986a
Guidelines for the Health Risk Assessment of Chemical Mixtures, Published on September 24, 1986, Federal Register 51(185):34014-34025, Risk Assessment Forum.
US Environmental Protection Agency
,
Washington, DC
.
USEPA
1986b
Guidelines for Carcinogen Risk Assessment
.
US Environmental Protection Agency
,
Washington, DC
.
EPA/600/8-87/045
.
USEPA
1989
Risk Assessment Guidance for Superfund
.
Vol. I
.
Human Health Evaluation Manual. Part A. Interim Final, Office of Emergency and Remedial Response
.
US Environmental Protection Agency
,
Washington, DC
.
USEPA
1994
Health Effects Assessment Summary Tables, FY-1994 Annual. Publication 9200.6-303(94-1), EPA540/R-94/020 PB94-921199, US Environmental Protection Agency, Washington, DC
.
USEPA
2000
Drinking Water Regulations and Health Advisories
.
US Environmental Protection Agency
,
Washington, DC
.
USEPA
2001
Risk Assessment Guidance for Superfund, Volume 1: Human Health Evaluation Manual (Part E, Supplement Guidance for Dermal Risk Assessment)
.
Office of Emergency and Remedial Response
,
Washington, DC
,
USA
.
USEPA
2004
Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment) Final. EPA/540/R/99/005 OSWER 9285.7-02EP PB99-963312 July 2004, Office of Superfund Remediation and Technology Innovation, US Environmental Protection Agency Washington, DC
.
USEPA
2013
Risk assessment IRIS (Integrated Risk Information System). http://www.epa.gov/risk_assessment
.
WHO (World Health Organization)
2004
Water Sanitation and Health Programme
.
Managing water in the home: accelerated health gains from improved water sources
.
World Health Organization
,
Geneva
,
Switzerland
. .
WHO (World Health Organization)
2008
Guidelines for Drinking-Water Quality
.
World Health Organization
,
Geneva
,
Switzerland
.
Wu
B.
Zhao
D.
Jia
H.
Zhang
Y.
Zhang
X.
Cheng
S.
2009
Preliminary risk assessment of trace metal pollution in surface water from Yangtze River in Nanjing Section, China
.
Bulletin of Environmental Contamintion and Toxicology
82
,
405
409
.
Yidana
S. M.
Yidana
A.
2010
Assessing water quality using water quality index and multivariate analysis
.
Environmental and Earth Sciences
59
,
1461
1473
.
Yin
Y. R.
Deng
Z. L.
2006
Analysis on relationship between drinking water and health
.
Scientific and Technological Information of China
219
221
.