Metal contamination assessment in water column and surface sediments of a warm monomictic man-made lake: Sabalan Dam Reservoir, Iran

In this study, metal concentrations in the water column and surface sediment of the Sabalan Dam Reservoir (SDR) were determined. Moreover, heavy metal pollution index (HPI), contamination index (CI), heavy metal evaluation index (HEI), enrichment factor (EF), geo-accumulation index ( I geo ), sediment quality guidelines (SQGs), consensus-based SQGs (C-BSQGs), and mean probable effect concentration quotients (mPECQs) were evaluated for water and sediments of SDR. It was observed that metal concentrations in river entry sediment were lower, but those in river entry water were higher than corresponding values in the vicinity of the dam structure. The HPI values of water samples taken from 10 m depth in the center of SDR exceeded the critical limit, due to high concentrations of arsenic. However, according to CI, the reservoir water was not contaminated. The HEI values indicated contamination of SDR water with metals at 10 m depth. A comparison of water quality indices revealed that HEI was the most reliable index in water quality assessment, while CI and HPI were not suf ﬁ ciently accurate. For SQGs, As and Cu concentrations in sediments were high, but mPECQ, I geo , and EF revealed some degree of sediment pollution in SDR. The calculated EF values suggested minor anthropogenic enrichment of sediment with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu. A comparison of SQG values revealed that the threshold effect and probable effect levels were the most reliable metrics in the assessment of sediment toxicity. Statistical analysis indicated similarities between metal concentrations in the center of the reservoir and near to the dam structure, as a result of similar sediment deposition behavior at these points, while higher ﬂ ow velocity at the river entry point limited deposition of ﬁ ne particles and associated metals. (cid:129) A comparison of metallic pollution indices in water revealed that heavy metal evaluation index was the most reliable index, while contamination index and heavy metal pollution index were not suf ﬁ ciently accurate. (cid:129) The calculated enrichment factor values suggested minor anthropogenic enrichment of surface sediment in the SDR with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu.

• A comparison of metallic pollution indices in water revealed that heavy metal evaluation index was the most reliable index, while contamination index and heavy metal pollution index were not sufficiently accurate.
• The calculated enrichment factor values suggested minor anthropogenic enrichment of surface sediment in the SDR with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu.

INTRODUCTION
Metal contamination, toxicity, and accumulation in the aquatic environments have attracted major global attention (Purves ; Valls & Lorenzo ; Gochfeld ; Vesali Naseh et al. ). Metals in the environment originate from both natural and anthropogenic sources, with the latter being more important. When metals are discharged into aquatic ecosystems, they redistribute into water and sediment strata. Due to adsorption, hydrolysis, and coprecipitation, large proportions of metals settle in bottom sediments, while only small proportions of free metal ions remain dissolved in the water column (Gaur et al. ).
In general, metal concentrations in sediments of an aquatic ecosystem are usually three-to five-fold greater than those in the water column (Luoma ). Therefore, concentrations of metals in sediments are a good indicator of the health and contamination status of water bodies (Pekey ). On the other hand, sediments act as a substantial sink and source of metals in water bodies (Morillo et al. ).
Accordingly, settled metals may be resuspended and released into the water column, causing ecological and health-related problems in aquatic ecosystems such as lakes and reservoirs (Saha et al. ; Tiwari & De Maio ). Therefore, in addition to studying metals in sediments, analyzing these pollutants in the water column provides important information for effective water quality management of aquatic environments, particularly dams, which are usually constructed to supply drinking water.
Many studies have examined the distribution of dissolved metals in water and sediments of natural lakes (Hou et  In this study, metal concentrations in the water column of a warm monomictic man-made lake, Sabalan Dam Reservoir (SDR) in Iran, were assessed using guidelines on drinking water quality. SDR water and sediment quality were then evaluated using well-known metal pollution indices to answer the research question: 'What is the level of metal pollution in SDR after around 13 years since impoundment?'. Additionally, water/sediment quality guidelines and indices were compared to determine which provided the most accurate results.

Study area
Sabalan Dam Reservoir, a warm monomictic man-made lake, is located at a mean elevation of around 1,000 m above sea level on the Qareh-Su river in Ardebil Province, supplies water for irrigation of around 15,000 hectares (ha) of agricultural land downstream of the dam and provides around 10 MCM of potable water annually for Meshkin Shahr City (population 150,000). In addition, the reservoir supplies water for 89 villages downstream of the dam.
Since the Qareh-Su river runs through different urban and rural areas and a wide range of agricultural and industrial land, it is exposed to large pollution loads, which makes SDR susceptible to water quality deterioration. Since the Sabalan Dam was built only recently, there is no water quality information available regarding metals in the reservoir. Therefore, this study evaluated metal concentrations in the water column and sediments of the reservoir to determine reservoir water quality 13 years after impoundment. Water samples were taken from surface water and at 10 m depth in the water column. All water samples were filtered using 0.45 μm membrane, transferred to polyethylene bottles previously cleaned with 50% HNO 3 , acidified with 50% HNO 3 to maintain pH < 2, and then stored in gray boxes at 4 C. Surface sediment samples were taken and transported to the laboratory, oven-dried at 80 C for 12 h, and then digested using a combination of HNO 3 , HCl, HF, and HClO 4 . Water and sediment samples were analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES) to measure the concentration of 13 metals. Analysis of the water samples was carried out using the U.S. EPA Method 3005A, Revision 1 (USEPA ), and analysis of the sediment samples was carried out using the U.S. EPA Method 3050B (USEPA ). Blanks and certified reference material (NIST 1640 and NIST 2709a for water and sediment, respectively) were used to assure quality control of the procedures. Duplicate analysis of blanks, samples, and certified reference materials revealed an acceptable recovery rate (92-104%) and relative standard deviation (SRD 5%).

Approaches for water/sediment evaluation
Analyzing the degree of metal contamination is compli-   Table S1. The metal concentrations detected in surface water and water samples taken from 10 m depth in SDR are shown in Figure 3.
The concentrations of metals in surface water and at 10 m depth followed the order: Fe > Al > Mn > As > Cu.
Concentrations of Cd, Co, Cr, Cu, Ni, Pb, V, and Zn were below the detection limit at all water sampling locations. It was found that the concentration of most dissolved metals was higher at 10 m depth than in surface water. In general, the SDR is thermally stratified during most months of the year and only mixes in late December to early March, i.e., it was thermally mixed during the sampling period. However, it was chemically stratified for various reasons. Compared with the thermal dynamics, the cycle of elements in SDR displays a lag, due to anoxic  anthropogenic sources of metals such as As in the region.
Therefore, it is of great importance to re-evaluate As concentrations in SDR water before starting water withdrawal, especially for drinking purposes.

Water quality guidelines
The Given the location of Point A close to the water gate, it is essential to take appropriate caution in the water withdrawal of SDR. In emergency cases, water should be withdrawn from the surface of the reservoir, where there is no concern about elevated As concentration.

Sediment analysis
Metal concentrations in sediment are shown in Figure 3.
The metal concentrations at Point A decreased in the East Dongting and Honghu lakes are known to be polluted with Zn and As (Makokha et al. ). Comparing the concentration of As in sediment of SDR with that in these two lakes, and also in the Earth's crust, showed that SDR sediments are slightly polluted with As (Table 2).

Water indices
The HPI values calculated for samples of surface water and water at 10 m depth are shown in Figure 5. The HPI values indicated that SDR water was polluted only at Point C, but the WHO and ISIRI guidelines were exceeded for As, with higher concentrations than the permissible limit, at Points A and C. Therefore, it can be concluded that HPI results should be verified using water quality standard guidelines before practical application, especially for drinking water.
The CI values in surface water and at 10 m depth in SDR were <1 at all sampling locations ( Figure 5), indicating no problem with metal contamination in SDR water. Therefore, the CI results were inconsistent with the HPI results and

Sediment indices
The I geo values for each element detected at the sampling locations are shown in Figure 6(a). The pollution status of sediments is evaluated using Supplementary Material, Table S2. Since the concentrations of Cd and Hg were below the detection limit, they were excluded from the calculation of I geo . From Figure 6(a), it can be concluded that sediments in SDR are generally not polluted with metals.
However, for Cu at Points A to C (I geo ≅ 0.05), sediments in SDR were found to be unpolluted to moderately polluted.
The calculated EF values were generally higher at Point C than at Points A and E (Figure 6(a)). Mn were within the range 3-5, classified as moderate anthropogenic enrichment (Birch ). This may be due to agricultural and industrial activities upstream of SDR.  The evaluation of sediments using the proposed SQGs revealed that As and Cu concentrations in sediments were a concern. The SQG values were also compared with each other to estimate the precision of each method (Figure 6(b)).
The comparison revealed that PEL values were lower than other upper boundaries in SQGs in all cases except for Cu.
This result shows that the PEL-TEL approach sets the most extreme boundaries for sediment toxicity and, hence, could be more reliable than other approaches. As seen in Figure 6( if a metal concentration was found to be non-toxic using PEL and TEL, it fulfilled the other two SQGs (LEL and SEL) in most cases. It can be concluded that PEL-TEL has a higher safety factor than the other two SQGs. On the other hand, the TEC values in most cases (except for Cu and Zn) were higher than the threshold effect values for the other SQGs.
Hence, it is necessary to apply appropriate caution when using the TEC-PEC approach.
The mPECQ values were calculated for As, Cr, Cu, Ni, Pb, and Zn concentrations at sampling stations across SDR ( Figure 6(a)), while Cd and Hg were below the detection limit and were excluded from the analysis. According to the mPECQ-based classification by Long et al. (), SDR is a low-medium priority site with a 15-29% probability of toxicity. However, the results are only valid for these six metals, which were used to get an indication of the toxicity risk in SDR sediments.
The mPECQ values declined on moving from the dam structure (Point A) to the river entry to SDR (Point F) ( Figure 6(a)). This finding means that the sediments located near the dam structure are more polluted with heavy metals than those at the point where the river enters SDR.

Statistical analysis
The normality of the data was checked by the Kolmogorov-Smirnov test (p < 0.05) before the application of statistical analyses. The results of statistical analyses are given below.

Water samples
The results of hierarchical CA by Ward's method for SDR water samples are given in Figure 7(a). Metals at Point A and B (surface water and 10 m depth) were grouped in one cluster and those at Points C to E in another cluster.
The third cluster included metals in surface water of Point F. These findings are a result of hydrodynamic conditions at the sampling points, as the turbulence declines from the river entry (Point F) to the dam structure (Point A).
The results of Pearson correlation analysis of metals in surface water and at 10 m depth are given in Table 3. Note that metals with concentrations below the detection limit were excluded from the correlation analysis. The results showed strong correlations between metals in surface water at Points A to D. Strong correlations were also observed for metals in surface water at Points D to F. Interestingly, it was found that on moving from Point A (dame structure) toward Point F (river entry), the correlation coefficient between concentrations in surface water and at 10 m depth increased. As discussed, differences in hydrodynamic conditions at the sampling points may have contributed to these differences.

Sediment samples
The results of hierarchical CA using Ward's method for metals in sediments of SDR are shown in Figure 7(b). Two clusters were observed, indicating similarities between metal concentrations at Points A to C (Figure 7(b)). This similarity is likely caused by similar deposition behavior of sediments at Points A to C, whereas higher flow velocity at Points D to F somewhat limits the deposition of fine particles, which are much more strongly associated with metals than coarse particles. The metals in the two clusters were (i) Al and Fe and (ii) Mn, V, Cu, As, Pb, Co, Cr, Zn, and Ni ( Figure 7(c)). Since Fe was classified in the same group as Al (cluster (i)), it can be concluded that it derives from lithogenic sources (Karbassi et al. ). The fact that Cu, Zn, Ni, and Cr were strongly correlated with each other (Table 4), and were classified in one cluster along with As, Pb, Co, V, and Mn, indicates that they may derive from the same source.
Pearson correlation was used to evaluate the sources of metals. Arsenic and V were excluded from correlation analysis due to their constant values in sediment. Correlation coefficients for metals in sediment (

CONCLUSION
Analysis of metal concentrations in water and sediment in SDR, a drinking water supply, revealed arsenic (As) to be the main concern in both water and sediment. Therefore, water withdrawal should be delayed until further analysis is performed to ensure that the As concentration meets the permissible value for drinking water. In emergency cases, water should be withdrawn from the surface of the reservoir, where the As concentration is lower.
Comparisons of various indices/guidelines for assessing metal pollution in water in SDR revealed that HEI was most reliable because both CI and HPI were unable to account for the effect of high concentrations in water of As, a critical pollutant. A comparison of SQGs revealed that the threshold effect and the probable effect were the most reliable guidelines for sediment quality assessment. This novel information can be used for effective water quality assessment/management in SDR.  *Correlation significant at p < 0.05 (two-tailed).