The Assessment of Health Risk from Heavy Metals with Water Indices for Irrigation and the Portability of Munzur Stream: A Case Study of the Ovacık Area (Ramsar Site), Türkiye

WQI is a simple and convenient approach to determining the overall quality of surface or groundwater and its potability. Assuming that Wi = wi/∑wi is the relative weight, Wi values (minimum 1 and maximum 5) were assigned to each parameter by considering the significant relative effect of heavy metals on human health and their significance in terms of portability. Table 1 shows that the highest weight applied to Mn, Hg, As, Pb, Cr and Cd, which have the most harmful effects on water quality (Gao et al. 2019).

Ci is the trace element concentration measured in the water. Si refers to the standard values determined by the WHO (2011) for drinking water. This was evaluated in accordance with WQI by Ramakrishnaiah et al. (2009), in which I (excellent) is an IWQI of ≤ 1.96, II (good) is 1.96 ≤ IWQI ≤ 5.88, III (average) is 5.88 < IWQI ≤ 9.80 and IV (poor) is > 9.80 (Maia and Rodrigues, 2012).

The heavy metal pollution index (HPI) proposed by Mohan et al. (1996) was used to estimate the overall pollution status of the water concerning heavy metals and metalloid elements. This method of indexing is based on the weighted arithmetic quality mean. The heavy metal pollution index (HPI) of Kumar and Singh (2018) is calculated with the following equation:

Here, Qi represents the sub-index of each metal, C represents the detected concentration value of metals, Si represents the standard values of parameters permitted by the WHO (2011) as drinking water, Wi represents the unit weight of metals and k represents the fixed value of ‘1’. Generally, pollution indices are developed for classifying water qualities for a specific purpose. The HPI was proposed to classify drinking water. The critical or permissible value was set as 100 (Mohan et al. 1996).

The indicator HEI measures contamination of water by heavy metals. Therefore, it helps in the easy interpretation of water pollution levels (Edet and Offiong, 2002). The HEI is computed using the formula below.

Here, HC stands for the value determined for each metal and H_MAC stands for the maximum allowed concentration value (MAC) of each metal (WHO, 2011). If HEI < 10, it is interpreted as low pollution; if 10 < HEI < 20, it is interpreted as medium pollution; and if HEI > 20, it is interpreted as high pollution (Saleh et al. 2018).

Heavy metals from freshwater are taken into the human body through ingestion or skin contact. Noncarcinogenic and carcinogenic health effects from oral intake and skin contact may be estimated by experimental models. This study used the health risk evaluation method recommended by the USEPA (2004), and the toxicological parameters of metals are shown in Table 1 (Wang et al. 2017). The average daily dose (ADD) by direct digestion (ADD_ingestion) and skin absorption (ADD_dermal) were computed using the formulas below (Zeng et al. 2015, Amiri et al. 2012b).

ADD_ingestion is the average digestive and dermal ingestion dose (μg kg⁻¹ day⁻¹); ADD_dermal is the daily average dose from digestion and through the skin (μg kg⁻¹ day⁻¹), C_water is the concentration of metals detected in the water (µg l⁻¹), IR is the ratio (2 for adults and 0.64 for children) (l day⁻¹), EF is the frequency of exposure (in this study, 365 days year⁻¹), ED is the exposure time (in this study, 70 days year⁻¹ for adults and 6 for children), BW is the average body weight (70 kg for adults and 20 for children in this study), AT is the average life expectancy (in this study, 25,550 days for adults and 2190 for children), SA is the skin area exposed (in this study, 18,000 cm² for adults and 6600 for children), ABS_g is the gastrointestinal absorption factor, KP is the dermal permeability coefficient in water (cm h⁻¹), ET is the exposure time during baths and showers (in this study, 0.6 hours day⁻¹) and CF is the unit conversion factor (1 l 1000 cm⁻³) (Saleem et al. 2019, Xiao et al. 2019).

Ingestion and skin ingestion of heavy metals are both possible carcinogenic and non-carcinogenic risks for children and adults. The non-carcinogenic risk ratio (hazard quotient [HQ]) of the ADD to the reference dose (RfD) was calculated. The hazard index (HI) is equal to the sum of the HQ values and represents all potential non-carcinogenic risks from heavy metals. HQ and HI were calculated with the following formulas.

A HQ of < 1 suggests that exposure to any adverse health effects is not likely, while an HI of > 1 indicates that there may be non-carcinogenic effects from heavy metal contact.

Excessively salty water is toxic to plants and creates a salinity hazard. Therefore, the quality of water used for irrigation is critical, since it affects the soil, plant and human health. SAR is generally considered to be a major index for assessing irrigation water. High sodium content in irrigation water leads to an alkali hazard and reduced soil permeability (Singh et al. 2020). A negative value of residual sodium carbonate (RSC) suggests the incomplete precipitation of Ca and Mg². The quality of irrigation water at Munzur Stream was assessed using the parameters SAR, %Na, RSC and MH, which were calculated with the following formulas (Ravikumar et al. 2013).

If mineral-rich water is regularly used for irrigation, the earth’s transmittance will diminish. The permeability index is calculated as follows (Falowo et al. 2017):

Factor, correlation and clustering analyses were performed in order to determine the processes occurring in the ecosystem identify the relationship between the calculated values and relate the sources. All stations are excellent according to the IWQI index.

In the study, descriptive statistical analysis of water quality parameters and whether there is a significant difference between stations was calculated using one-way analysis of variance (ANOVA) (p < 0.05). PCI was applied to determine the direction and degree of correlation between parameters, while CA was used to investigate the similarities and differences. PCA was used to reduce the dataset and reveal new factors. All these statistical analyses were carried out with the statistical software programme statgraphics (XVI).

The physicochemical parameters of drinking water and irrigation water reveal the natural quality of the water. Table 2 shows the results of the evaluation of the general physicochemical parameters of Munzur Stream, a sensitive water body area, following the WHO (2011) and Turkish guidelines (2016). The change wasn’t observed in the colour, smell or taste. The pH value was higher than the values determined as the limits (6.8 and 8.5) by the WHO (2011) and the TSWQR (Rasol et al. 2017, Ali et al. 2017, Arshad and Imran 2017). The pH value was in the 3^rd Class according to TSWQR (2016) (Table 1). S5 had the highest pH, while S6 had the lowest value. Munzur Stream is classified as alkaline water. The fact that the river basin has a karstic structure has contributed to this. Surface water temperature and atmospheric temperature changes occur simultaneously with each other. As a result, it was concluded that aquatic life did not reach a temperature beyond dangerous limits in any month of the year. In terms of temperature, S1 was the lowest, while S9 was the highest. In terms of temperature, it has features of Class I water (Kutlu et al. 2017).

EC is an indicator of water salinity. Pollutants may raise the EC value of surface waters (Şener et al. 2017). If the EC value rises, when this water is used as irrigation water, the plants will not be able to compete with the ions in the soil solution, which may lead to a physiological drought (Naseem et al. 2010). High EC values are associated with animal farms, agricultural waste and sewage and discharge waste (Kanhabam et al. 2017). The EC values of Munzur Stream were within the acceptable limit range, according to the WHO (2011). While the lowest EC value was at S1, the highest was found at S7. Additionally, in the study, there was an increase in the EC values along with increased temperature. The EC rises as a result of higher salinity with evaporation (Jiang et al. 2015, Zhang et al. 2016).

In regard to the total dissolved solids, it has been reported that the main difference between the characteristics of lakes and streams is related to the distribution, composition and relative concentration of the substances along the stream. It has also been reported that the geochemical structure of the drainage basin and the seasonal changes in runoff and falling precipitation influence the composition of the streams (Reid 1961, Jain 2002). While the TDS value was the lowest at S1, the highest was detected at S5. According to the WHO and TSWQR, this is in Class I. In this study, it was determined that the sodium concentration (Na) values of Munzur Stream were within the WHO (2011) limits. The highest value in the sampling area was detected at S3, while the lowest one was detected at S1. Cation change is related to the geological characteristics of the solution of lithogenic sodium (Guo et al. 2007, Rafigue et al. 2008). The magnesium concentration was within the WHO (2011) concentration limits. The highest value was observed at the end station of the stream (Station 9), and the lowest value was observed at the origin of the water. Magnesium is the most abundant element in the earth’s crust. It is normally found in mineral rocks and enters waters by natural or anthropogenic means. High amounts of magnesium negatively affect the health of humans (Daud et al. 2017, Rasol et al. 2017). According to the SWOR (2016), it has been said that phosphate values do not exceed the limits given in TSWQR Class III. While the phosphate value was the lowest at S8, the highest was found at S3. It is thought to be high as a consequence of the direct delivery of domestic waste without water treatment, along with the fact that the fertilisers used in agricultural areas are phosphate-based.

The main sources delivering nitrites to surface and ground waters are degraded plant and animal waste, domestic wastewater, fertilisers used in agriculture, industrial wastewater and leaching of nitrogen from the atmosphere. In sufficiently nitrified domestic wastewater, when added to the environment it causes high nitrite values. The lowest nitrite amount was (0.005) in the Munzur Stream region. Meanwhile, the highest amount (0.029 mg l⁻¹) at S5 can be attributed to the degradation of organic matter, the sewage waters mixed with the stream in the lower stream region and the domestic waste from the villages in the region. Munzur Stream is a Class 3 at S5 and S6, while it is Class 2 at other stations in terms of the nitrite value according to the classes of intra-continental water resources. On the other hand, according to the WHO, S3, S5 and S6 were higher than the WHO values, and the others were below the upper limit (Belal et al. 2016). Additionally, methemoglobinemia, blue baby syndrome, stomach cancer, abnormal pain, central nervous system disorders and diabetes in animals and humans are caused by a high concentration of nitrates in drinking water (Belal et al. 2016). In the study area, the nitrate level in the water was much lower than the nitrate values of drinking water in many places (Soomro et al. 2017). Additionally, since it is below the WHO (2016) values, it does not pose a danger to the health of humans.

Ammonium, on the other hand, is highly toxic to fish along with pH and temperature (Debels et al. 2005). Ammonium, which is an organic biodegradation product, is a nitrogen product that is directly used by plants. Farmers commonly use ammonium sulphate fertilisers as organic and inorganic fertilisers in agricultural fields (Vega et al. 1998). Irrigation water carries the fertilisers into lakes and ponds. While the ammonium value was the lowest at S1, the highest was found at S3. This results in an excessive increase in the level of nitrogen and phosphorus in the rivers. It also causes excessive proliferation of phytoplankton at the first level of the food chain. The sources of heavy metals of lotic ecosystems can be natural or anthropogenic. The natural sources are generally controlled by the geology and lithology of the region through which the river passes, and include processes such as bedrock weathering and erosion, soil leaching, volcanic eruption and atmospheric precipitation. Anthropogenic sources of these pollutants include mining, metal smelting and refining, landfill leachates, agricultural runoff and industrial and domestic effluent (Ustaoğlu et al. 2020a, b).

According to trace metals and inorganic parameters, Munzur Stream is in the high-quality class except for Cr and Pb (TSWQR, 2016). In terms of concentration, its sources and distribution and whether the heavy metal in the water poses a health risk are very important. The average concentrations of major ions and heavy metals were Ca > Mg > Al > Fe > Pb > Cr > Cd > Na > Zn > Cu > Ni > As > Mn > Co. With the exception of lead and chromium, it is quite good in terms of metals. The water quality of the basin is also in very good condition owing to the absence of factories in the study area, which can lead to industrial pollution. The high level of lead may be due to the geomorphological structure of the Ovacık region and pesticides (Önal et al. 2020). However, all sewage of Ovacık Province flows into the river, and agricultural waste flowing into the river cause pollution, especially during dry periods.

On the other hand, chromium may originate from solid waste leachates other than mining operations. It may mix with water sources and can be found in drinking water due to galvanised pipes. Drainage water and runoff from paddy fields may have possibly resulted in a major increase in Cd and Pb concentrations at some stations (Tokatli and Varol 2022). Agricultural runoff is likely to have increased Cd concentration in places. While the cadmium value was the lowest at Station 1, the highest was found at Station 2. Phosphate fertilisers used in agricultural fields are a major source of Cd (Rutigliano et al. 2019). It is believed that the reason for the high level of these metals may be the agricultural and surrounding mineral deposits, along with anthropogenic waste and rocks (Kumar et al. 2017). Parallels can be seen in the results of this study and other studies (Fatmi et al. 2009, Podgorski et al. 2016). If heavy metal pollution in rivers is not addressed at the right time, it will affect the freshwater ecosystem and human health in the future (Töre et al. 2021). The high level of cadmium in this region may be due to the fault line, as the sampling site is on the Ovacık fault line (Sancar et al. 2019).

The Water Quality Index (WQI) was used to determine the water quality of Munzur Stream. To compute the index, the concentrations of pH, EC, Tds, Alk, Th, Cl, DO, Ca, Mg, Mn, Cu, Cd, Pb, Ni, Zn, Cd, Pb, Ni, Zn and As were determined. The WHO (2011) limits were used, as shown in Table 2. The WQI values found in the stream were between 36.61 and 50.14. The water quality parameters did not exceed the recommended limits in any month and or any station. According to these values, Munzur Stream falls into the excellent and good categories in terms of the quality of drinking water. The lowest value was found at Station 1, whereas the highest value was found at Station 3. It has the ideal water feature according to the drinking water quality (Table 4).

The combined effect of the heavy metals in the river was determined by the HPI and the HEI. Global standard values were used in the calculations (WHO, 2011). It was determined that the HPI value exceeded 100 at S1 and S2. The maximum values of HPI were 114.56 and 111.40, which were found at S1 and S2. They may be attributed to gold mine sand domestic waste complex effluents. The HPI at this station was higher than the critical index value of 100, indicating critical contamination with heavy metals. At the other station, the value was calculated as greater than 100, particularly in the downstream parts. This includes S1, S2 and S3. The overall pollution level was low (Saleh et al., 2018). In this study, heavy metal analysis and quality assessment of the drinking water of Munzur Stream in Türkiye were performed. The results show that the HPI values in all samples was less than 100, and thus the drinking water quality was good in terms of heavy metal content (Fig. 2).

Figure 2

Similarly, the HEI values were also calculated to determine the metal (−loid) pollution load in the stream water. The HEI values ranged from 1.81 to 3.90 at the stations. All the values of HEI in both seasons were less than 10, which indicates low metal (−loid) pollution (Fig. 2) (Prasanna et al. 2012, Bodrud-Doza et al. 2016).

Heavy metals in drinking water are a danger to health and can cause various types of cancers and noncarcinogenic conditions. In this study, the noncarcinogenic/carcinogenic health risk of heavy metals in children and adults was investigated. The ADD (dermal and ingestion), HQ (dermal and ingestion), and HI values were calculated by means of the toxicological values of each metal (Table 3) (USEPA, 2004, Wang et al. 2017). According to the HQ standards, when the HQ value is greater than 1, adverse health effects (noncarcinogenic risk) may occur in humans. Pb level had the highest HQ ingestion values in adults and children (0.029 × 10⁻⁶; 0.029 × 10⁻⁵, respectively) and had the highest HI values (0.05; 0.05, respectively). In the present study, all HQ and HI values were less than 1 in adults and children. As a result, Munzur Stream is safe for public health for residential uses.

The health risks of surface waters of Munzur Stream were calculated using the USEPA risk assessment models. The intake and skin absorption of seven heavy metals were computed using the HQ (Fig. 2). It is appropriate that these values are not greater than 1. In this study, the HI values for children were greater than those for adults. However, the exposure value for both adults and children was less than 1. The total metal pollution HI is as follows: Pb > Cu > Cd > Pb > Ni > Hg > Fe.

Sodium absorption ratio (SAR): The values of EC and SAR were used to determine the quality of irrigation water. The SAR value of Karaca Dam was 0.05 mEq l⁻¹ (Table 4). If the SAR value is > 9, the sodium level is hazardous and the water cannot be used for irrigation (Awais et al. 2017). In this study, the SAR value was within the prescribed limits. High SAR values might lead to lower salinity values, which would result in a decrease in the calcium and magnesium levels. These are essential elements for the growth of plants (Vasantvigar et al. 2010, Rasool et al. 2016). The study shows that Munzur Stream is suitable for irrigation (Table 5).

Sodium percentage (%): Several researchers have classified water on the basis of the sodium percentage (Srinivas et al. 2017; El Aziz, 2017; Islam et al., 2017). In this study, the average percentage of sodium was found to be 50% (Table 4). According to Table 4, the dam water was in the permissible water quality category. The use of fertilisers in agriculture might result in the presence of a high proportion of sodium in the water. A high level of sodium (9%) in the land has adverse effects on the tightness, ventilation and texture of the soil (Singara et al. 2014). Plants that are subjected to water containing high amounts of sodium show a low fertility rate because sodium affects the osmotic pressure between the soil and the plant. Therefore, it blocks the plant’s mineral uptake from the soil (Nasseem et al. 2010).

Permeability index (PI): The presence of excess minerals such as Ca, Mg, Na and HCO₃ in water affects the permeability of the soil. The long-term agricultural productivity is also greatly affected by this (Sing et al. 2008). The PI has a negative effect on the fertility of the soil (Obiefuna and Sheriff 2011). The use of water rich in minerals inhibits the growth of seedlings by blocking ventilation in the soil. The Permeability Index was developed by Doneen (1975) to classify water. According to this index, water that has a PI of > 75 is in the first class, a PI of 25–75 denotes second-class water and a PI of < 25 is in the third class (Doneen 1975, Raju et al. 2011). In this study, the PI was found to be 5.73. Thus, the region has first-class water (Table 4).

To determine the risk, the value of RSC was calculated; the indicator was in the range of −21.6 to 33. All the spatial and temporal parameters contribute to good water quality (RSC < 1.25) of irrigation water. In a study conducted on Munzur Stream, the irrigation water for all stations was reported to be excellent, good and suitable in terms of SAR, Na% and MH, respectively (Özer and Köklü 2019). The IWQI index of all stations was calculated, and the water can be used as irrigation water.

Assessment of contamination sources between physicochemical parameters of Munzur Stream: Pearson’s correlation analysis was performed to determine the trend and correlation between the physicochemical parameters, as presented in Table 6. In this study, there was a significant positive relationship between the basic parameters (pH, EC, TS, Alk, TH, Cl and DO). The TDS value and alkalinity in Munzur Stream were in a negative direction. Manganese, cadmium and lead showed a strong positive correlation. The contamination of these pollutants in the water may be the result of agricultural activities, domestic wastewater and aquaculture activities.

Principal Component Analysis (PCA) is helpful, as it contracts the dimensions of multivariate data to help in understanding the problems and their contributing factors. The size of the data matrix of PCA was 9 samples and 15 variables. In this study, only five principal components (PCs) were found to describe 92.28% of the total variance in Munzur Stream (Tables 5 and 6). In Munzur Stream, 33.17% of the variance of the total dataset is explained by PC1, 26.27% of the variance by PC2 and 14.58%, 10.14% and 8.11% by PC3, PC4 and PC5, respectively. The factor loadings are generally grouped on the basis of their absolute loading values. Absolute loading values of > 0.75 are considered ‘strong’, values of 0.75–0.50 are considered ‘moderate’ and the range of 0.50–0.30 is considered ‘weak’ (Liu et al. 2003, Wang et al. 2017, Kumar et al. 2018). PCA was carried out to trace the origins of heavy metals and element concentrations in natural waters.