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Vertical and horizontal distribution, source
identification, ecological and toxic risk assessment
of heavy metals in sediments of Lake Aygır, Kars,
Turkey
Serkan Kükrer
To cite this article: Serkan Kükrer (2018) Vertical and horizontal distribution, source identification, ecological and toxic risk assessment of heavy metals in sediments of Lake Aygır, Kars, Turkey, Environmental Forensics, 19:2, 122-133, DOI: 10.1080/15275922.2018.1448905
To link to this article: https://doi.org/10.1080/15275922.2018.1448905
Published online: 13 Apr 2018.
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Vertical and horizontal distribution, source identi
fication, ecological and toxic risk
assessment of heavy metals in sediments of Lake Ayg
ır, Kars, Turkey
Serkan K€ukrer
Department of Geography, Faculty of Social Science and Humanities, Ardahan University, Ardahan, Turkey
ABSTRACT
Surface and core sediment samples were collected from Lake Aygır, Turkey, to determine heavy metal distribution, probable sources and potential ecological and toxic risks for the lake. Heavy metals, total sulfur, total phosphate, total organic carbon, chlorophyll degradation products, and CaCO3content were established. The enrichment factor, PLI, potential ecological risk index, and
toxic risk index were calculated. Zn was determined to have the highest accumulation in surface sediment, followed by Cr, Pb, and Cd, respectively. Cd was the only element that exceeded the critical value of 40 and posed a moderate potential ecological risk. According to TRI, no ecotoxic risk was found. It is thought that local fossil fuel consumption is responsible for the accumulation of heavy metals since there is a lack of urbanization, industrialization, and agricultural activities around the lake.
KEYWORDS
Environmental risk assessment; heavy metal; sediment; Lake Aygır
Introduction
Heavy metals, some of which are used in industry and some of which have important roles in metabolic activi-ties (Cu, Se, Zn, etc.), are natural components of the Earth’s crust (Uwah et al., 2013). Although they meet human needs, heavy metals from both anthropogenic and natural sources pose a serious threat to the envi-ronment and human health in excessive quantities due to their retention, toxicity, and bioaccumulation (Duman et al., 2007; Tao et al., 2012). They are the focus of worldwide concern due to their negative effects on human health via consumption of aquatic organisms in public water supplies (Tao et al.,2012).
Pollutant metals introduced into the environment by human activities are classified by their sources: metals from domestic wastewater inputs such as As, Cr, Cu, Mn, and N; metals from coal burning power plants such as As, Hg, and Se; those from metal dis-charge units such as Cd, Ni, Pb, and Se; metals from iron and steel plants such as Cr, Mo, Sb, and Zn; and metals from waste sludge such as As, Mn, and Pb (Nriagu and Pacyna, 1988). Modern farming methods can also cause As and Cd accumulation (Bai et al.,
2011; 2012).
Sediment quality is a good indicator of water pol-lution (Zahra et al., 2014). Suspended sediment
absorbs pollutants from the water. The heavy metal content of sediments is closely related to the grain size of the sediment and organic carbon content of the environment. Smaller grain size and higher organic carbon content increase the amount of heavy metals in the sediment. Heavy metals are inert in the sediment and are considered as conservative pollu-tants; however, in certain conditions, they can cause ecological damage (Yi et al., 2011). Accumulated heavy metals in the sediment may be released into the water as a result of changes in oxidation and reduction, pH, organic/inorganic carbon and dissolved oxygen concentrations (¸Cevik et al., 2009; Wang et al.,2012). Thus, even if they do not pose an imme-diate threat to aquatic life, they present potential threats for its future.
Defining these potential dangers and monitoring time-dependent risk changes in the ecosystem are of key importance in considering whether precautionary measures need to be taken. For this purpose, various ecological and biological indexes should be used to carry out rapid evaluation. These include the enrich-ment factor (EF), contamination factor, and geo-accu-mulation index for determining whether the metal accumulation is natural or anthropogenic; the pollu-tion load index (PLI) and potential ecological risk
CONTACT Serkan K€ukrer kukrerserkan@gmail.com Ardahan University, Faculty of Social Science and Humanities, Department of Geography. Yenisey Campus, 75000 Ardahan, Turkey.
Color versions of one or more of thefigures in the article can be found online at www.tandfonline.com/uenf.
© 2018 Informa UK Limited, trading as Taylor & Francis Group
VOL. 19, NO. 2, 122–133
index for identifying ecological threats; and the mean probable effect concentration quotient (mPEC-Q) for determining biological risks, all of which are fre-quently used indexes in various studies (Long et al.
2006; Bing et al. 2013; Hou et al. 2013; Yang et al.
2014; Zahra et al. 2014; K€ukrer et al. 2015).
The study area is freshwater Lake Aygır, 13 km from the city of Kars, Turkey on the Kars-G€ole road and west of Susuz district. It has a surface area of 2,948 ha and lies at an altitude of 2,300 m. It was formed on volcanic rocks and is fed by snowmelt and by source waters at its bottom. The lake is surrounded by stony mountain steppe and dip slopes. Its rapidly increasing depth and sparse vegetation on the slopes reduce biological diversity in the area (Anon-ymous,2017). This area is geologically part of the volcanic region of the Kars-Erzurum Plateau (northeastern Turkey). This extensive volcanic area forms the Anatolian part of the South Caucasus region that extends from the Javakheti Plateau (Georgia) in the north to the Lori Plateau (northern Armenia) in the south (Rolland,2017). This broad domain is made up of the thick basalt lava outcrops of Late Pliocene to Early Pleistocene (»3.25–2.05 Ma) age (Sheth et al.
2015). On the other hand, the onset of collision-related vol-canic activity in the area might have occurred earlier (11 Ma) (Keskin,1995,1996).
Lake Aygır is situated in a high-altitude area of Kars province, Turkey and is not surrounded by intense urbanization or industrialization. Fishing is the main occupation of the local community. The purpose of this study is: a) investigation of the verti-cal and horizontal distribution of heavy metals at Lake Aygır, where there has been no study up to now on heavy metal distribution, b) determination of rela-tions with environmental parameters (organic carbon, carbonate, etc.) affecting the transport and sedimenta-tion processes of heavy metals, c) identificasedimenta-tion of possible sources of heavy metals, and d) calculation of potential environmental risks of heavy metals found. The results will contribute to the literature by evaluating changes from past to present and by deter-mining the trend of heavy metal concentrations.
Materials and methods
A 66 cm-long core sample was taken from a station (St 5) near the middle of the lake with a Kajak gravity core sampler to determine vertical distribution and surface sediment samples were collected from five sta-tions using a Van Veen grab to establish horizontal distribution and ecological and toxic risk assessment
(Figure 1). Five rock samples were also collected from the surrounding area in order to compare heavy metal concentrations in the sediment with nearby rock samples. The core sediment was sliced at 2 cm intervals. All samples were dried in an oven at 60C and were pulverized in a mortar. Organic carbon was determined in accordance with the Wakley-Black titration method (Gaudette et al., 1974). Total CaCO3 content was measured through a Scheibler calcimeter (Schlicting and Blume, 1966). Chlorophyll degrada-tion products (CDP) in the surface sediments were analyzed following acetone extraction (Lorenzen,
1971).
Rock and sediment samples were sent to ACME Inc. Laboratory (Vancouver, Canada) for multi-element anal-yses, which were carried out by ICP-MS (inductively coupled plasma mass spectrometry). For quality control, duplicate sample readings, blank samples, and ACME internal standard reference materials were used. Results obtained from the reference sample analysis are shown inTable 1.
Sediment dating was conducted by Beta Analytic Inc. Laboratory (Florida, FL, USA) and carbon 14C dating was applied to the organic carbon from the bottom layer (64–66 cm) of the core.
EF was used to determine the source of the accu-mulation in the sediment. The EF is obtained by dividing the observed metal/Al ratio by background metal/Al ratio (Acevedo-Figueroa et al. 2006; ¸Cevik et al. 2009; Yilgor et al. 2012; Vrhovnik et al. 2013). Al is used in this calculation because it is considered conservative and independent of anthropogenic effects. Metal values were divided to Al values across the core sediment. Obtained values were standard-ized with log 10 transformation in order to fit nor-mal distribution and a nornor-mal probability curve was drawn for each variables. The averages of the origi-nal equivalents of the minimum values cluster in the
graph were used as background value ( €Ozkan and B€uy€ukı¸sık, 2012). The EF was evaluated in accor-dance with (Sutherland, 2000): EF <2, minimal or no enrichment; EF = 2–5, moderate enrichment; EF = 5–20, significant enrichment; EF = 20–40, very high enrichment; and EF >40, extremely high enrichment.
The PLI was used to determine the environmental quality of the sediment (Suresh et al.2011):
PLI ¼ CF1ð x CF2x . . . CFnÞ16 n
where CF is the contamination factor (ratio of metal con-centration to background metal concon-centration) and n is the number of metals.
The Potential Ecological Risk (PER) index was calcu-lated to assess the potential risks posed by the metals (Hakanson, 1980). The PER was calculated for each metal as follows:
Eif¼ Cif x Tif
where Tifrepresents the response coefficient for the tox-icity of an individual metal and Cif represents the con-tamination factor. The toxicity coefficients of the metals are: Hg (40), Cd (30), As (10), Cu, Pb, and Ni (5), Cr (2), and Zn (1) (Guo et al.2010).
Integrated PER values were calculated as follows: PER ¼ S Eif
The following classification was used to assess the risk factor (Hakanson1980): Eri< 40 low potential ecological risk, 40 Eri< 80 moderate potential ecological risk, 80 Eri< 160 considerable potential ecological risk, 160 Eri< 320 high potential ecological risk, Eri 320 very high ecological risk, PER< 150 low ecological risk, 150 PER< 300 moderate ecological risk, 300 PER< 600 Table 1.Values obtained from Reference materials (mg/g for all element except TS and TP for which % is used.).
Sediment analyses Rock analyses
Element Observed value Expected value Detection limits Observed value Expected value Detection limits Cu 149.49 154.61 0.01 7715.6 7705 0.5 Pb 154.89 150.55 0.01 8618.1 8715 0.5 Zn 360.7 370 0.1 10,728 10,750 5 Ni 72.2 74.6 0.1 3455.6 3420 0.5 Mn 938 875 1 3892 4100 5 Fe 28,100 27,188 100 77,000 77,500 100 As 49.2 43.7 0.1 45 46 5 Cd 2.89 2.49 0.01 47.7 50 0.5 Cr 49.8 54.6 0.5 182.1 172 0.5 Hg 0.321 0.300 0.005 0.57 0.6 0.05 Mo 12.81 14.69 0.01 — — — Al 10,100 10,259 100 10,300 10,200 100 TS 0.30 0.29 0.02 — — — TP 0.08 0.07 0.001 — — — Nb 1.06 1.0 0.02 — — — Ti 710 817 10 — — —
Table 2. Descriptive statistics of variables (m g/g for all variables except TS, TP,CaCO 3 ,OC, and Ti for which % is used). Sam ple Cu Pb Zn Ni Mn Fe As Cd Cr Hg Mo Al TS TP CaCO 3 OC Nb Ti Aver age 18.50 11.64 33.11 25.16 414.22 13625 4.20 0.22 19.06 0.07 1.36 13269 0.29 0.06 21.91 8.03 1.30 0.023 Min . 7.04 3.89 10.8 10.1 268 5000 2.1 0.08 7.4 0.04 0.54 3800 0.12 0.04 1.22 3.55 0.53 0.011 Cor e Max . 27.16 17.57 50.5 35.5 1241 19800 7.1 0.37 28.4 0.129 2.81 19500 0.46 0.11 94.73 13.09 1.64 0.033 Stan dard error 0.99 0.72 2.36 1.37 35.22 818.3 0.21 0.014 1.15 0.004 0.11 783.8 0.02 0.00 3.55 0.48 0.05 0.00 Coe ff. of variat ion 0.31 0,35 0.40 0.31 0.48 0.34 0.28 0,37 0.34 0.32 0.47 0.33 0.32 0.24 0.92 0.34 0.23 0.27 Backg round 23.41 8.56 14.7 31.9 385 9850 3.3 0.18 11.9 0.06 — 13269 —— — — — — Ro ck material Aver age 9.28 § 1.13 1.05 § 0.39 15 § 2.48 3.67 § 0.33 79.5 § 9.75 8500 § 672 < 5 < 0.5 2.85 § 0.53 < 0.05 — 7325 § 1500 —— — — — Cu Pb Zn Ni Mn Fe As Cd Cr Hg CDP Al TS TP CaCO 3 OC Aver age 19.34 12.51 38.84 24.38 386.00 13740.00 4.18 0.25 19.18 0.04 115.6 6 13960.00 0.36 0.08 26.02 5.61 Min . 13.03 9.13 30.90 17.30 350.00 10400.00 2.90 0.16 14.30 0.02 23.87 11500.00 0.10 0.05 0.01 2.88 Su rface Max . 23.89 15.68 44.90 30.90 454.00 18800.00 6.30 0.35 26.80 0.06 204.1 7 19200.00 0.71 0.14 56.22 9.01 Stan dard error 2.00 1.11 2.82 2.30 18.92 1449.00 0.65 0.04 2.21 0.01 34.45 1351.52 0.10 0.02 10.09 1.16 Coe ff. of variat ion 0.23 0.20 0.16 0.21 0.11 0.24 0.35 0.37 0.26 0.43 66.60 0.22 0.66 0.47 0.87 0.46 0.66 0.10
considerable ecological risk, PER 600 very high eco-logical risk.
Toxic risk index (TRI) was also used to assess ecotoxic risks (Zhang et al.2016). The TRI for an individual metal
is calculated by the following formula:
TRIi¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð Ci=TELð Þ2þ Ci=PELð Þ2Þ
2 s
The integrated toxic risks of metals in sediment are calculated by the following formula:
TRI¼X n i¼1
TRIi
where ere TRIiis the TRI of a metal, Ciis the heavy metal concentration measured in the sample, n is the number of metals, and TRI is the total TRI. The following classifi-cation was used for TRI: TRI 5 no toxic risk, 5 < TRI 10 low toxic risk, 10 < TRI 15 moderate toxic risk, 15< TRI 20 considerable toxic risk, TRI > 20 very high toxic risk. TEC and PEC for freshwater ecosystems (MacDonald et al.,2000) were used instead of TEL and PEL in this study.
Multivariate methods such as principal component analysis, cluster analysis, and Pearson’s correlation test were applied to the data to determine possible heavy metal sources. Cluster analysis was carried out by near-est-neighbor method.
Figure 2.Vertical distribution of variables (% for TS, TP, OC, and Ti,mg/g for other variables) in core sample.
Figure 3.Vertical variation of Mo/Al and Nb/Ti values.
Table 3.Concentration of elements (% for TS and TP,mg/g for others) in surface sediment of Lake Aygır.
Cu Pb Zn Ni Mn Fe As Cd Cr Al Hg TP TS St 1 16.91 11.28 40.1 24.9 351 14,200 2.9 0.16 20.5 13,200 0.035 0.139 0.1 St 2 19.85 12.69 33.8 21.9 390 11,500 4.9 0.35 15.4 11,500 0.045 0.061 0.45 St 3 23.89 15.68 44.5 26.9 454 13,800 6.3 0.35 18.9 13,400 0.062 0.104 0.71 St 4 13.03 9.13 30.9 17.3 350 10,400 2.9 0.17 14.3 12,500 0.035 0.056 0.28 St 5 23.03 13.79 44.9 30.9 385 18,800 3.9 0.24 26.8 19,200 0.016 0.049 0.24
Table 4. Comparison of heavy metal values in surface sediment of Lake Ayg ır( m g/g) from other lake sediments in Turkey. Lake Cu Pb Zn Ni Mn Fe As Cd Cr Al Hg Refe rences U luabat, Turkey 119, 2 110,7 171 209, 4 0,699 57,9 Ar slan et al. 2010 Mog an, Turkey 15,1 3 0,822 13,78 6 — 125,6 68 3577 —— 28,55 —— Ben zer et al. 2013 Ko vada, Turkey 4,65 –13,77 1,74 –4,42 12,82 –33,42 9,13 –25,93 61,19 –165,96 3006 –7345 — BDL-0,2 7 6,63 –17,59 3780 –9990 — Kı r e t al. 2007 Su dotelis,Lithuan ia — 3.67 —— — — — 0.21 —— — Pal iulis 2014 Bo yuk-Shore ,Aze rbaijan 14.7 28.5 86.8 — 355 —— 1.7 25.1 — 0.0085 Kha lilova and Mamm adov 2016 Do nghu, Ch ina 184.8 –229.3 27.7 –58.7 352.9 –391.2 —— — 46.4 –56.8 2.16 –2.32 13.8 –20.1 —— Ntaki rutimana et al. 2013 ¸Cı ld ır, Turkey 20.3 4 11.14 49.1 8 30.2 0 478. 67 15767 3.45 0.22 27.62 0.06 K€ ukre r e t al. 2014 At at €urk Dam, Turke y 14.57 –22.7 BDL 59.14 –60.79 43.69 –139.69 73.6 –514.07 12587 –19265 BDL BDL Karad ede and Unl €u2000 Bafa ,Turke y 5.79 –55.1 2.31 –23.5 26.62 –79.9 1.21 –320 250 –780 9.38 –33.1 (g/k g) —— 63.1 –277 — 0.01 3– 0.260 Yilg or et al. 2012 Ayg ır Lake ,Turkey 19.34 12.51 38.84 24.38 386 13740 4.18 0. 25 19.18 13960 0.04 Thi s stud y Note .BDL: Belo w dete ction limit. Bo ld values highlights the result s o f this study.
Results and discussion
Sediment characterization and distribution of elements in sediment
Descriptive statistics of all variables from the core sedi-ment samples are given inTable 2.
General trends of variables across the core revealed that the levels of all elements, except Hg and Mo, were higher in the first 40 cm in comparison with those in the bottom section (Figure 2). Although Cu, Pb, Zn, S, Cd, As, Cr, and Mo values showed a ten-dency to decline between 20 cm and the present-day sediment, small increases on the sediment surface were observed for these metals. Mn, TP, and TS exhibited an increasing trend from 10 cm to the pres-ent day. Although showing an increase between 6 and 20 cm, Hg showed a declining trend toward the sedi-ment surface (Figure 2). It is suggested that volcanic material, having a high Nb/Ti rate due to incompati-ble elements such as Nb, is enriched in this material in comparison with Ti (Johnson et al., 2002; Garcin et al., 2007). Nb/Ti distribution in the sediments of Lake Aygır showed some peaks at the 20–40 cm and 50–60 cm intervals and these increases are compatible with increases determined in the vertical distribution of Cu, Pb, Zn, Fe, Al, Ni, As, Cd, Cr, and Hg. These heavy metal increases detected at Lake Aygır, which is located in a volcanic region, may have come from volcanic material stored in the terrestrial area.
The Mo/Al ratio was used to identify anoxic condi-tions. Under conditions where organic material is over-accumulated, aerobic respiration mechanisms are exhausted and an anoxic state emerges, then Mo dis-solved in the water bonds with S and precipitates in the sediment, hence the Mo/Al ratio increases (Adelson et al., 2001). In Lake Aygır, Mo/Al levels peaked at around 40 cm and rapidly decreased from there to the present day (Figure 3). The transition to toxic conditions decreased the Mo concentration and increased the Mn concentration instead. Factors such as bacterial respira-tion, chemical oxidarespira-tion, morphometry, and lake pro-ductivity in ice-covered lakes could lead to oxygen depletion (Mathias and Barica,1980; Babin and Prepas,
1985; Ellis and Stefan, 1989; Golosov et al.,2007). The Mo increments detected in Lake Aygır, as a seasonally ice-covered lake, may have appeared for the reasons listed above. The similarity between organic carbon and Mo/Al distribution supports this hypothesis.
According to the 2 sigma calibrated radiocarbon test result, the bottom layer of core sediment was dated to between Cal AD 1445 and 1525 (Cal BP 505 to 425).
The horizontal distribution of elements was found to be different (Table 3). Minimum values were
determined in St 4 for Cu, Pb, Zn, Ni, Mn, Fe, and Cr while minimum values were found in St 1 for Cd, in St 2 for Al, and in St 5 for Hg. Minimum concen-tration of As was observed in St 1 and St 4. Maxi-mum values of heavy metals were detected for Cu, Pb, Mn, As, and Hg in St 3, and for Zn, Ni, Fe, Cr and Al in St 5. Cd reached its maximum in St 2 and St 3. Stations where the minimum and maximum val-ues were determined are common for Cu, Pb, Mn, As, and for another group including Zn, Ni, Fe, and Cr. TS shows significant correlation with Mn, As, and CDP. The high correlation between TS and some metals is associated with the formation of insoluble sulfides (Bai et al.,2016, Zhang et al., 2016). The rela-tionship between TS and CDP might indicate sulfur compounds produced by phytoplankton. It is known that microalgae cells may contain sulfur compounds in different forms (Lomans et al., 1997). The high correlation between OC and CDP shows that the plant biomass living in the lake is the main source of OC.
The spatial variability of metals is determined by coef-ficient of variation (CV) (Hu et al. 2008). Accordingly, spatial variability is grouped as follows: weak when CV%10%, moderate at 10% <CV% < 100%, and strong when CV% 100%. According to this grouping, the metals measured in Lake Aygır showed moderate variability.
Measurements for heavy metal content in surface sed-iment were compared with those of lake sedsed-iments from other regions of Turkey and worldwide (Table 4). The Cu content of Lake Aygır was higher than that of lakes
Table 5. Correlation matrix of variables (num bers in bold indicate statistically signi ficant correlations at 95 % con fidence interval). Cu Pb Zn Ni Mn Fe As Cd Cr Al Hg OC CaC O3 CDP TP TS Cu 0,9788 0,819 9 0,8442 0,8175 0,65 03 0,7780 0,7163 0,59 19 0,491 0 0,1923 0,3342 ¡ 0,4996 0,3003 ¡ 0,0099 0,55 63 Pb 0, 9788 0,800 3 0,7685 0,903 6 0,52 79 0,8627 0,7622 0,46 91 0,345 1 0,3782 0,4814 ¡ 0,5321 0,4145 0,1084 0,67 25 Zn 0,8199 0,8003 0, 9518 0,5630 0,84 98 0,4176 0,2235 0,83 42 0,677 7 ¡ 0,030 1 ¡ 0,0573 ¡ 0,7998 ¡ 0,162 3 0,3178 0,18 79 Ni 0,8442 0,7685 0,9518 0,4507 0,9407 0,3410 0,2403 0,9209 0,786 3 ¡ 0,234 2 ¡ 0,1866 ¡ 0,7081 ¡ 0,248 9 0,1341 0,06 40 Mn 0,8175 0,9036 0,563 0 0,4507 0,17 18 0,9730 0,8420 0,10 47 0,050 8 0,6675 0,7609 ¡ 0,2903 0,7237 0,0592 0,9157 Fe 0,6503 0,5279 0,849 8 0, 9407 0,1718 0,0418 ¡ 0,0355 0,9964 0,9324 ¡ 0,537 9 ¡ 0,4889 ¡ 0,5740 ¡ 0,492 6 ¡ 0,0111 ¡ 0,2111 As 0,7780 0,8627 0,417 6 0,3410 0,973 0 0,04 18 0, 9385 ¡ 0,0312 ¡ 0,09 06 0,7189 0,8477 ¡ 0,1889 0,8139 0,0048 0,9425 Cd 0,7163 0,7622 0,223 5 0,2403 0,8420 ¡ 0,0355 0,9385 ¡ 0,1145 ¡ 0,15 07 0,6126 0,8193 ¡ 0,0037 0,8255 ¡ 0,1827 0,86 37 Cr 0,5919 0,4691 0,834 2 0, 9209 0,1047 0,9964 ¡ 0,031 2 ¡ 0,1145 0,9277 ¡ 0,572 6 ¡ 0,5471 ¡ 0,5935 ¡ 0,560 9 0,0313 ¡ 0,2818 Al 0,4910 0,3451 0,677 7 0,7863 0,0508 0,9324 ¡ 0,090 6 ¡ 0,1507 0,9277 ¡ 0,687 0 ¡ 0,5903 ¡ 0,2720 ¡ 0,487 3 ¡ 0,2983 ¡ 0,2588 Hg 0,1923 0,3782 ¡ 0,0301 ¡ 0,234 2 0,6675 ¡ 0,5379 0,7189 0,6126 ¡ 0,5726 ¡ 0,68 70 0,9379 ¡ 0,1114 0,8049 0,3925 0,80 93 OC 0,3342 0,4814 ¡ 0,0573 ¡ 0,186 6 0,7609 ¡ 0,4889 0,8477 0,8193 ¡ 0,5471 ¡ 0,59 03 0, 9379 0,075 2 0,9487 0,0880 0,9241 Ca CO3 ¡ 0,499 6 ¡ 0,5321 ¡ 0,7998 ¡ 0,708 1 ¡ 0,2903 ¡ 0,5740 ¡ 0,188 9 ¡ 0,0037 ¡ 0,5935 ¡ 0,27 20 ¡ 0,111 4 0,0752 0,3356 ¡ 0,7936 0,04 98 CD P 0,3003 0,4145 ¡ 0,1623 ¡ 0,248 9 0,7237 ¡ 0,4926 0,8139 0,8255 ¡ 0,5609 ¡ 0,48 73 0,8049 0,9487 0,335 6 ¡ 0,2221 0,9356 TP ¡ 0,009 9 0,1084 0,317 8 0,1341 0,0592 ¡ 0,0111 0,0048 ¡ 0,1827 0,03 13 ¡ 0,29 83 0,3925 0,0880 ¡ 0,7936 ¡ 0,222 1 ¡ 0,0825 TS 0,5563 0,6725 0,187 9 0,0640 0,915 7 ¡ 0,2111 0,9425 0,8637 ¡ 0,2818 ¡ 0,25 88 0,8093 0,9241 0,049 8 0,9356 ¡ 0,0825
Mogan, Boyuk-Shore, and Kovada, whereas it was signif-icantly lower than that of lakes Donghu, ¸Cıldır, and Uluabat; and it had values similar to those of the other lakes. The Pb levels were higher than those of lakes Mogan, Kovada, Sudotelis, and ¸Cıldır; within the given range for Lake Bafa; and lower than that of lakes Donghu, Buyuk-Shore, and Uluabat. The Zn values were lower than those of lakes Uluabat, ¸Cıldır, Donghu, Buyuk-Shore, and Atat€urk; in compliance with the given ranges of Lake Bafa; and higher than those of Mogan and Kovada lakes. The As concentration was higher than that of Lake ¸Cıldır while it was lower than that of Lake Donghu. The Cd value was lower than that of Uluabat, Buyuk-shore, and Donghu; in compliance with the given range of Lake Kovada; and higher than Sudotelis and ¸Cıldır. The Hg concentration was lower than that of Lake ¸Cıldır; in compliance with the given ranges of Lake Bafa; and it was higher than that of Lake Buyuk-Shore.
Assessment of heavy metal accumulation using enrichment factor (EF)
The EF) is an important tool for identifying possible heavy metal sources. This method enables determi-nation of whether the heavy metal source is natural or anthropogenic. Box plots of EF values in surface sediment from Lake Aygır are given in Figure 4. EF values, in descending order, were Zn, Cr, Pb, Cd, Fe, As, Mn, Cu, Ni, and Hg. The EF values ranges were
0.59–1.01 for Cu, 1.11–1.81 for Pb, 2.11–3.00 for Zn, 0.58–0.84 for Ni, 0.69–1.17 for Mn, 1.12–1.45 for Fe, 0.82–1.89 for As, 0.89–2.24 for Cd, 1.28–1.73 for Cr and 0.18–1.01 for Hg. It can be suggested that EF values less than 0.5 are due to mobilization of the metal (Zhang 1995), values ranging 0.5–1 indicate
that the metals have natural sources, and EF values higher than 1.5 indicate anthropogenic sources (Zhang and Liu,2002). Zn and Cd were the elements that reached a value of > 2, which indicates moder-ate enrichment, while Pb, As, and Cr show minimal anthropogenic enrichment in some stations. The other elements were determined to have natural sources.
Assessment of pollution using pollution load index (PLI)
PLI values were between 0.03 and 4.34, and the mean PLI value was 1.23. The PLI values for each station were as follows: 0.19 for St 1, 0.51 for St 2, 4.34 for St 3, 0.03 for St 4 and 1.10 for St 5. The highest value was observed in St 3, whereas the lowest value was observed in St 4. Suresh et al. (2011) stated that the ideal PLI value repre-senting lack of pollution was zero, 1 was the baseline, and values above 1 represented the presence of pollution. Accordingly, the surface sediments of Lake Aygır can be considered polluted at minimal level. This level is caused by the high heavy metal accumulation in St 3, such as of Pb, Zn, As, and Cd. The high metal concentrations in this station are probably due to higher surface water flows in this area.
Assessment of potential ecological risk using PERI
The Eifvalues are presented inFigure 4. The risk indices of metals are ranged as follows: Cd> Hg > As > Pb > Cu > Ni > Cr > Zn. According to average values, the limit value is exceeded only for Cd, which shows moder-ate risk, whereas the potential ecological risk index for other elements remains at low levels. Hg reached its Figure 5.Dendogram of cluster analysis.
Table 6.Proximity matrix of cluster analysis.
Cu Pb Zn Ni Mn Fe As Cd Cr Al Hg Cu 0 0,169 1,441 1,246 1,46 2,798 1,776 2,27 3,265 4,072 6,462 Pb 0,169 0 1,597 1,852 0,771 3,777 1,098 1,902 4,247 5,239 4,974 Zn 1,441 1,597 0 0,385 3,496 1,202 4,659 6,212 1,327 2,578 8,241 Ni 1,246 1,852 0,385 0 4,394 0,475 5,272 6,078 0,633 1,709 9,873 Mn 1,46 0,771 3,496 4,394 0 6,626 0,216 1,264 7,162 7,593 2,66 Fe 2,798 3,777 1,202 0,475 6,626 0 7,665 8,284 0,029 0,541 12,303 As 1,776 1,098 4,659 5,272 0,216 7,665 0 0,492 8,249 8,725 2,249 Cd 2,27 1,902 6,212 6,078 1,264 8,284 0,492 0 8,916 9,205 3,099 Cr 3,265 4,247 1,327 0,633 7,162 0,029 8,249 8,916 0 0,578 12,581 Al 4,072 5,239 2,578 1,709 7,593 0,541 8,725 9,205 0,578 0 13,496 Hg 6,462 4,974 8,241 9,873 2,66 12,303 2,249 3,099 12,581 13,496 0
highest value in St 3 and showed moderate ecological risk only at this station. Even though the integrated PERI val-ues achieved maximum level in St 3, this indicates low ecological risk. The risk value of the current sediment is distinctly below the critical threshold of 150.
Assessment of ecotoxic risk using TRI
TRI values were between 2.00 and 3.37. Average TRI value was 2.76. The highest TRI value was determined in St 3 while the lowest value was observed in St 4. All TRI values indicate that there is no toxic risk in the surface sediments of the lake. The highest contribution to TRI derives from Ni and is followed by Cu and Cr, respectively.
Identification of possible sources of heavy metals
Correlation and Cluster analyses were used to identify possible sources of heavy metals. The correlation matrix is given in Table 5. The majority of the metals were found to be related to each other, which indicates simi-larities in transport and source (Wang et al.,2012). Pb shows a strong correlation with Cu and Mn, while Zn has a significant relationship with only Ni. Ni indicates a connection with Fe and Cr. A high correlation was deter-mined between Fe, Cr, and Al, which points to a litho-genic source. The high correlation of As with Cd and Mn appears to show a common source. The relationship between Hg and OC indicates the role of OC in the Hg transport process.
The results of the cluster analysis seem to be consis-tent with the correlation testfindings. Accordingly, Cu-Pb and Mn-As-Cd are clustered together, which indi-cates a common source and migration. On the other hand, Zn and Ni exhibit a strong association, based on a similar process, whereas Fe, Cr and Al create another cluster, as lithogenic elements. Hg has a different source and process from all the other elements (Figure 5,
Table 6).
The heavy metal content in the rock samples is equal to or slightly less than the background values (Table 2). The Zn and Cd values in surface sediment are suggestive of an anthropogenic input, contrary to the other ele-ments with natural sources.
Conclusions
Zn was determined to have the highest accumulation in the surface sediment, followed by Cr, Pb, and Cd, respec-tively. It is thought that heavy metal increases in the mid-dle layers of the core sediment are associated with input of volcanic material from terrestrial sources. Cd was the
only element that exceeded the critical value of 40 and posed a moderate potential ecological risk. Average PLI values, which are indicators of the sediment’s quality, point to a polluted sediment at minimal level, due to the high accumulation in St 3. According to TRI, no ecotoxic risk was found. It is thought that fossil fuel consumption in local settlements is responsible for the accumulation of heavy metals, as there is a lack of urbanization, indus-trialization, and agricultural activities around the lake. More widespread use of environmentally friendly fuels is expected to reduce metal accumulation and potential risks in the sediment.
Conflict of interest
The author declares no conflict of interest.
Acknowledgment
I would like to thank Drs. Ahmet Evren Erginal, Mustafa Kar-abıyıkoglu and ¸Caglar ¸Cakır for their valuable contributions and also Abdullah Akba¸s for drafting the location map. I thank the three anonymous reviewers for their helpful comments. Graham Lee is thanked for proof-reading the earlier version of the text. This study was supportedfinancially by the Research Foundation of Ardahan University (project number: 2015–12), for which I am grateful.
Funding
Research Foundation of Ardahan University (project number: 2015-12).
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