Next Article in Journal
Lifelong Learning as a Factor in the Country’s Competitiveness and Innovative Potential within the Framework of Sustainable Development
Next Article in Special Issue
Correction: Yozukmaz, A.; Yabanlı, M. Heavy Metal Contamination and Potential Ecological Risk Assessment in Sediments of Lake Bafa (Turkey). Sustainability 2023, 15, 9969
Previous Article in Journal
Towards a New Paradigm of Project Management: A Bibliometric Review
 
 
Correction published on 2 November 2023, see Sustainability 2023, 15(21), 15561.
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Heavy Metal Contamination and Potential Ecological Risk Assessment in Sediments of Lake Bafa (Turkey)

Department of Aquatic Sciences, Faculty of Fisheries, Mugla Sitki Kocman University, 48000 Mugla, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(13), 9969; https://doi.org/10.3390/su15139969
Submission received: 19 May 2023 / Revised: 14 June 2023 / Accepted: 19 June 2023 / Published: 22 June 2023 / Corrected: 2 November 2023
(This article belongs to the Special Issue Heavy Metal Pollution and Ecological Risk Assessment)

Abstract

:
This study examined the spatio-temporality of heavy metal concentrations (Al, Cd, Co, Cr, Fe, Mn, Ni, Pb and Zn) in the sediments of Lake Bafa, one of the most important wetlands of Turkey’s Aegean region. The study evaluated sediment quality according to threshold effect concentration (TEC) and probable effect concentration (PEC) values based on sediment quality guidelines (SQG), and provided a potential ecological risk assessment (PERI) along with indices such as geoaccumulation index (NIgeo), enrichment factor (EF), contamination factor (CF), and pollution load index (PLI). For this purpose, surface sediment from 10 different points and core samples from three different points were seasonally collected and the concentrations of nine heavy metals were determined by ICP-MS. The findings indicated the following accumulation order of heavy metals in the sediment: Fe > Al > Mn > Ni > Cr > Zn > Pb > Co > Cd, with concentrations of Al, Mn, and Ni being high in the surface sediment samples. According to the NIgeo, surface sediment and core samples were very slightly polluted with Cr, Mn, and Co at most stations, while five stations were slightly polluted with Cd. Regarding EF, the lake was at risk in terms of Al and Pb accumulation. The CF results indicated that the lake was under pressure in terms of heavy metal pollution. The PLI results indicated a significant pollution hazard at all stations, while the PERI analysis indicated moderate risk of heavy metal pollution at some stations. As one of the most comprehensive studies applying such indices to Lake Bafa, the results are very significant in terms of evaluating the lake’s ecological sustainability.

1. Introduction

During the 20th century, rapid population-driven socio-economic development led to an increase in urbanization, industrial development, and agricultural activities, and a concomitant increase in demand for clean water resources [1,2,3]. The industrial revolution caused a significant increase in pollution in aquatic ecosystems to become the most important current environmental issue [4,5,6,7,8,9].
The pollutants that adversely affect natural ecosystems enter the aquatic ecosystem from various sources and are exposed to physical, chemical, and biological processes [10]. These processes directly depend on the structure of metals in their environment, their concentrations, and the metabolic activities of living organisms exposed to this pollution. Other physical factors, such as suspended solids (SS), temperature, dissolved oxygen, and pH, also affect the circulation properties of pollutants [11]. The most dangerous pollutants for the natural environment are those that remain intact for a long time in their environment, cannot be assimilated, and have highly toxic effects [12,13].
Such pollutants include heavy metals, which can be toxic to organisms at high concentrations [14], do not decompose, and infuse up the food chain into higher level organisms to accumulate in various tissues and organs in a process called bioaccumulation. The ultimate accumulation levels in equivalent tissues and organs in different organisms can vary depending on their structures, leading to different alterations in particular tissues and organs [15]. Once concentrations of heavy metals in an ecosystem increase beyond trace amounts, living organisms cannot metabolize them efficiently. Therefore, their bioaccumulation increases faster than their environmental concentrations, leading to the transportation of heavy metals to higher levels in the food chain [4,16,17]. As a result, when heavy metals exceed their natural concentrations in aquatic ecosystems, they have many different adverse effects that limit the vital functions of living organisms.
Having entered the aquatic ecosystem in many different ways, heavy metals infuse into the food chain through various environmental components (water, sediment, seston, etc.) and living organisms (micro and/or macro vertebrates and invertebrates), or through non-nutritive ways (e.g., respiration, absorption through the skin, adsorption) [18,19]. Although these processes do not directly damage living organisms, heavy metals accumulate in different tissues and organs due to bioaccumulation and complex food chain interactions [20,21]. This bioaccumulation occurs because the rate of metabolic removal of pollutants directly or indirectly taken in by living organisms in aquatic ecosystems is slower than their uptake rate. The term bio-concentration refers to the level of substances that living organisms take directly from the water through different tissues and organs (gills, epithelial tissue, etc.) which then accumulate in tissues and organs (muscle, kidney, liver, etc.) [22,23,24]. Due to bioaccumulation, living organisms that perform their vital functions in aquatic ecosystems can accumulate pollutants at much higher amounts than the pollutants’ concentrations in the water itself [25].
After entering the aquatic ecosystem, heavy metals do not remain in the water column for long if their concentrations are high. Instead, they settle into the sediment [26,27,28]. While heavy metals adsorbed in sediment are not a direct source for aquatic organisms, they can be released back into the water column due to environmental changes (e.g., in temperature, salinity, pH, redox potential) occurring in the constantly dynamic water column above the sediment [29]. Consequently, sediment acts as a renewable resource for aquatic ecosystems for such pollutants [30]. Furthermore, due to their structure, both organic and inorganic pollutants can endure aquatic environmental conditions and are not decomposed by physical, chemical, and biological processes. Therefore, they can accumulate in the sediment layer over many years to pose both a direct and indirect threat to the health of humans and aquatic organisms. The sediment layers of aquatic ecosystems affected by urbanization contain particularly high levels of pollutants [31,32] that lead to severe environmental problems [33,34]. Therefore, it is important to protect sediment quality to ensure the sustainability of aquatic life and ecological balance, and to biologically protect water bodies that cross national borders, whether small or large in volume. Pollutants in the sediment can threaten or even eliminate aquatic species by damaging the food chain. Due to physical, chemical, and biological processes occurring in the sediment, pollutant groups can also be transferred into the water layer and living organisms due to bioaccumulation along the food chain [35].
Thus, given that the quality of the sediment layers helps determine the quality of the water column above it [36,37], environmental sedimentology studies and water quality analyses should be carried out simultaneously. More specifically, all pollutants (organic substances, phosphates, nitrogenous compounds, and various metals) have specific saturation levels in the sediment layer. Once their sedimentary concentrations reach this level, they are released into the water as a pollutant source. Thus, it is not enough to focus on solving pollution limited to the water column because pollution can reoccur due to the release of pollutants from the sediment [38]. Chemical analysis can be used to determine the sediment layer’s environmental risk level and establish standard quality criteria for sediment quality. The pollutant concentrations can be compared to the toxicity levels in living organisms in the sediment and the substances that affect them [39]. In short, regular examination of sediment is important in determining the level of risk in aquatic ecosystems.
The present study examined the spatio-temporal concentrations of nine heavy metals (Al, Cd, Co, Cr, Fe, Mn, Ni, Pb, and Zn) in the sediments of Lake Bafa, which is one of the most important wetlands of Turkey’s Aegean region. Sediment quality was evaluated according to threshold effect concentration (TEC) and probable effect concentration (PEC) values based on sediment quality guidelines (SQG), and with a potential ecological risk assessment (PERI), along with the following four indices: geoaccumulation index (NIgeo), enrichment factor (EF), contamination factor (CF), and pollution load index (PLI). Lake Bafa was selected as the sampling area because it has a unique aquatic ecosystem connected both to the sea and the groundwater system, and is affected by several anthropogenic factors, particularly agricultural, domestic, and industrial waste. In addition, with the current study, it will be possible to have an idea about how the accumulation of toxic pollutants in aquatic ecosystems with similar ecological characteristics in the world will affect the quality of lake sediment.

2. Material and Method

2.1. Area of Study

The sampling area of this study was Lake Bafa, located in Turkey’s Aegean region between the provinces of Aydın and Muğla (37°30′ N, 27°25′ E). The lake’s water surface covers approximately 6708 ha. The lake’s north-south width is 4.5 km, while its east-west length is 15.4 km. The lake surface is 10 m above sea level, while its deepest point is 21 m. The lake’s most important freshwater sources are surface and underground waters from the Büyük Menderes River and the Beşparmak Mountains around the lake’s north-east shore [40].
Lake Bafa was once a bay connected to the Aegean Sea. Marine conditions were dominant in the lake until the Hellenistic period [41], when the seaward connection was lost due to alluvial transport in the Büyük Menderes River, which formed a natural barrier (coastal dam) lake ecosystem over a period of almost 6000 years. The region where Lake Bafa is located has always been an important center for civilizations throughout history as both a marine ecosystem and then a freshwater ecosystem. Nowadays, the lake is a tourist attraction due to its historical features drawn from many different civilizations and its biological diversity.
In 1985, an earthen embankment was built by the General Directorate of State Hydraulic Works at the point where the Büyük Menderes River enters the north-west side of the lake. Given that it is the largest river in Western Anatolia with a total length of 584 km that flows through Afyon, Denizli, Uşak, and Aydın provinces before discharging into the Aegean Sea [42], the embankment separated Bafa Lake from its main fresh water source and caused irreversible changes to its ecosystem. In 1994, the lake and surrounding forestland were designated as a national park named “Menderes Delta National Park”. In addition, the areas within the park containing archaeological artifacts were designated as Grade 1 Protected Areas [43]. In recognition of its rich biodiversity, Lake Bafa is also listed as an Area of Special Interest under the Ramsar and Bern international conventions.
Cutting the inflow of fresh water from the Büyük Menderes River led to an increase in salinity levels that has gradually transformed all the lake’s fauna and flora. In addition, the large human population (approximately 2.5 million people live alongside the Büyük Menderes River) and surrounding small-scale industrial activities (especially olive oil factories) have led to the discharge of both domestic and industrial waste into Lake Bafa [44].

2.2. Sample Collection

The sampling strategy was designed to include bottom sediments of all major river courses by referring to similar studies conducted in the region. Accordingly, sediment samples were seasonally collected between December 2013 and November 2014 from 10 predetermined stations (Figure 1 and Table 1).
The sediment samples were collected from 5–10 cm below the sediment surface using an Ekman bottom sampler (15 × 15 × 20 = approximately 225 cm2) and placed in acid-cleaned glass containers. Three additional samples (C1, C2 and C3) were taken in 1 month using a 10-cm sediment core sampler, and examined in 2 pieces of 5 cm length. This allowed depth-wise variations in accumulation levels and particle size to be calculated. All samples were placed in an ice box during transportation in accordance with the standards and stored in appropriate laboratory conditions until the date of analysis.
In addition, measurements were taken at each sampling station of the lake’s main physico-chemical parameters (temperature, pH, salinity, dissolved oxygen (DO), total dissolved solids (TDS), and conductivity) using a multiprobe water quality measurement device (YSI Professional Plus). Finally, measurements were also made in the lab of the lake’s water nutrient levels (nitrite nitrogen, nitrate nitrogen, ammonium nitrogen, and phosphate phosphorus) using a DR 3900 spectrometer with suitable ready-to-use kits.

2.3. Heavy Metal Analysis in Sediment

Sediment samples were collected from each station seasonally for 1 year between December 2013 and November 2014 and brought to the laboratory under appropriate conditions (in a cooler with a mean temperature of 4 °C). The sediment samples, which were stored under suitable conditions (at −18 °C) until pretreatment were removed from the deep freezer and thawed. Sub-samples of 5 g from each sample were mixed in glass containers pre-cleaned with acid, then weighed and resolved to 0.5 g aqueous sediment solutions. For heavy metal analysis, they were dissolved in 3 mL of hydrochloric acid, nitric acid, and water (HCl–HNO3–H2O) solution (at a ratio of 3:1:2) at 95 °C for 1 h and diluted with 10 mL of distilled water. The obtained colorless solution was centrifuged at 4000 rpm for 10 min and left to cool. The supernatant was then carefully taken using a syringe and transferred to capped falcon tubes of appropriate volume. Distilled water was added to each dissolved sample to reach a final volume of 15 mL, and the samples were made ready for analysis. Measurements were made using an Agilent brand 7700× model inductive coupled mass spectrometry (ICP-MS) [45,46,47]. The device’s detection limits for sediment are shown in Table 2.
The accuracy and precision of the heavy metal analysis results in the ICP-MS were checked with standard reference material (Sigma-Aldrich® CRM016- Fresh Water Sediment 3 for Trace Metals) (Table 3).
All the data obtained were further processed with the ArcGIS Pro Desktop application and turned into a map to show the geographically significant status (see Supplementary File).

2.4. Particle Size and Heavy Metal Analysis in Sediment Core Samples

Particle size analyses (PSA) were performed to detect heavy metal accumulation trends in sediment core samples. For particle size measurements, 3 core samples (13 cm on average) were divided into 5 cm slices. Each slice was then divided into sub-samples of 5 g, of which 3 were dried in an oven at 105 °C for PSA. Each dried sample was then homogenized individually in a porcelain mortar and sieved serially through 5 mm, 0.3 mm, and 0.063 mm mesh sieves. This process demonstrated that there were no particles larger than 5 mm, so the PSA groups were defined as follows [48]:
(a)
>0.30 mm = coarse sand;
(b)
0.30–0.063 mm = fine grain sand;
(c)
<0.063 = clay.
The weight ratios of the three groups in the total sediment were calculated by weighing. Each group in each sample was then pretreated to make it ready for ICP-MS analysis.

2.5. Determination of Organic Carbon Amounts in Sediment Samples

The organic carbon amount in the sediment samples was determined with the Walkley–Black method. This is based on the combustion of all organic matter in the samples using potassium dichromate (K2Cr2O7) and concentrated sulfuric acid at appropriate concentrations during the heat treatment of the samples, and back titration with ferro ammonium sulfate and phenylamine indicator. The carbonates and bicarbonates in the sediment samples were removed using 10% HCl. To determine the carbon content, approximately 0.2–0.5 g was taken from each sample and placed in a glass flask thoroughly cleaned with acid. Back titration was then applied with adjusted bichromate solution and iron ammonium sulfate solution [49].

2.6. Assessing the Contamination Status of the Sediment

The geoaccumulation index (NIgeo) is a scale developed to indicate pollution levels in coastal sediment due to anthropogenic terrestrial heavy metal accumulation [50,51]. It is calculated according to the following formula:
N I g e o = l o g 2 ( C n 1.5 × B n )
where Bn refers to the current heavy metal concentration in unpolluted sediment. Cn refers to the current heavy metal concentration in the sediment sample and the coefficient (1,5) refers to the possible changes from terrestrial effects. The obtained results are categorized as follows: “NIgeo < 1, unpolluted; 1 < NIgeo < 2, very slightly polluted; 2 < NIgeo < 3, slightly polluted; 3 < NIgeo < 4, moderately polluted; 4 < NIgeo < 5, very polluted; NIgeo > 5, very much polluted”. In the present study, the background values were based on the results from core sample number 2, which had the lowest concentrations for all heavy metals.
Like the geoaccumulation index, the enrichment factor (EF) is a scale used to determine the lithogenic effects on heavy metal concentrations in sediment [52]. It is calculated according to the following formula:
E F = C x C n e S a m p l e C x C n e B a c k g r o u n d
where Cx refers to the metal concentration calculated by the enrichment factor; Cne refers to the concentration of the normalizing element. When calculating EF, conservative elements such as Al and Fe, which are naturally found in high concentrations in the structure of the lithosphere, are used as normalizing elements [53,54]. A result of 0.5 ≤ EF ≤ 1.5 indicates that heavy metal accumulation occurred due to natural processes, whereas a result of EF > 1.5 means that heavy metal accumulation resulted from anthropogenic processes [55]. In the present study, Fe concentrations were used as the normalizing element.
The contamination factor (CF) also evaluates heavy metal accumulation in sediments by comparing its level to preindustrial reference levels [56]. It is calculated according to the following formula:
C F = C ( m e t a l ) C ( b a c k g r o u n d )
where C(metal) refers to the concentration of the sampled metal and C(background) stands for the reference control value. As explained above, in the present study, heavy metal concentrations in the sediment sample taken from the 10 cm depth of the C2 core sample were used as background. CF values are categorized into four levels of contamination: “CF < 1 = low contamination; 1 ≤ CF < 3 = moderate contamination; 3 ≤ CF < 6 = considerable contamination; and CF ≤ 6 = very high contamination” [54].
The pollution load index (PLI) also measures heavy metal pollution in sediments [57,58]. PLI is calculated using the following formula:
P L I = ( CF 1 × CF 2   × CF 3 ×   × CF n ) 1 n
where CF is the contamination factor for each heavy metal under consideration, calculated according to Equation (3). PLI values are categorized as follows: “1 > PLI = no contamination; PLI = 1 = baseline levels of contamination; and 1 < PLI = deterioration of site quality” [54].

2.7. Assessing the Potential Ecological Risk (PERI) of Sediment

Sediment quality guidelines (SQG) are used to understand whether heavy metals accumulating in the sediment pose an ecological risk. However, SQG has not previously been used to assess this risk in wetland samples (for both marine and freshwater ecosystems) in Turkey. Therefore, threshold effect concentration (TEC) and probable effect concentration (PEC) values taken from internationally accepted SQGs were used to compare the sediment quality of the samples in the present study [59,60,61]. TEC shows the heavy metal concentration below which negative ecological effects are not anticipated to occur [59]. Concentrations which are equal to or above the TEC but below the PEC define the range within which ecological effects rarely occur, whereas concentrations above the PEC indicate the range within which negative ecological effects are likely to occur often [60].
In addition, the potential ecological risk index (PERI) [56,61,62] is calculated to predict the potential effects of heavy metals on aquatic ecosystems. The index can reveal potential relationships (synergy, toxicity level, and ecological sensitivity) between all the assessed heavy metals [54] whose concentrations were determined in the study. PERI is calculated using the following formulae:
P E R I = i = 1 n E r i
E r i = T r i × C r i
where n is the number of heavy metals; i is the heavy metal of interest in the sediment; E r i is the potential ecological risk coefficient of a single heavy metal; and T r i is the toxic response factor for the heavy metal of interest” [54,63]. T r i values for Cd, Cr, Hg, Mn, Pb, and Zn were 30, 2, 40, 1, 5, and 1, respectively. C r i represents the calculated CF for each metal. E r i values are interpreted as follows: “ E r i < 40 = low risk; 40 ≤ E r i ≤ 80 = moderate risk; 80 ≤ E r i < 160 = considerable risk; 160 ≤ E r i < 320 = high risk; E r i ≥ 320 = very high risk” (Decena et al., 2018). PERI values are categorized as follows: “PERI < 90 = low risk; 90 ≤ PERI <180 = moderate risk; 180 ≤ PERI <360 = strong risk; 360 ≤ PERI < 720 = very strong risk; and PERI ≥ 720 = very high risk” [56].

2.8. Statistical Analyzes

Pearson correlation tests were performed to reveal the significance of the relationship between the physico-chemical variables and the heavy metal concentrations. Analysis of variance (ANOVA) was applied to test whether there were significant differences between stations in heavy metals concentrations. Principal component analysis (PCA) was used to determine the relationship among all the environmental variables and the heavy metal concentrations. All analyses were performed with the IBM® SPSS Statistics® 24.0 program. A value of p < 0.05 was selected as significant.

3. Results

3.1. Analyses on Heavy Metal Concentrations in Surface Sediment and Core Samples

The results of heavy metal analysis (Al, Cr, Co, Ni, Zn, Cd, Pb, Fe, and Mn) for the sediment and core samples taken between December 2013 and November 2014 showed that the mean heavy metal concentrations in the surface sediment of Lake Bafa ranked as follows from highest to lowest: Fe > Al > Mn > Ni > Cr > Zn > Pb > Co > Cd. The detailed results for each element are presented below.
Aluminum (Al): The lowest aluminum concentration (54.03 mg kg−1) was observed at Station 10 in summer, while the highest (7251. 38 mg kg−1) was at Station 1 in summer. Al accumulation at Station 10 was significantly different from that at Stations 1, 2, 3, and 5 (p < 0.05). Seasonally, mean Al concentrations in the spring and summer differed from the fall and winter seasons. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Al levels. The findings showed that Al tends to bind most to sediment particle sizes of <0.063 mm.
Manganese (Mn): The lowest manganese concentration (197.05 mg kg−1) was at Station 8 in winter, while the highest (1331.45 mg kg−1) was at Station 2 in fall. Mn levels did not differ seasonally or between sampling stations. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Mg levels. The results showed that Mn tended to bind to sediment particles of <0.063 mm. Sediment particles of >0.3 mm at 5–10 cm depth adsorbed Mn element at high levels.
Iron (Fe): The lowest iron concentration (22,308.95 mg kg−1) was detected at Station 9 in spring, while the highest (41,345.00 mg kg−1) was detected at Station 8 in spring. Fe element accumulated in various regions and point sources. However, there were no significant differences in terms of stations and seasons. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Fe levels. The results showed that Fe tended to bind to sediment particles of <0.063 mm in the core sample from 5–10 cm depth and adsorbed to sediment particles of 0.3–0.063 mm in the core samples from 0–5 cm.
Chromium (Cr): The lowest chromium concentration was LOD (below the analysis limits) at Station 10 in summer, while the highest (330.82 mg kg−1) was at Station 7 in spring. There were no significant seasonal or spatial differences in Cr levels. Cr accumulated at certain point sources. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Cr levels. The results showed that Cr tended to bind most to sediment particles of <0.063 mm.
Cobalt (Co): The lowest cobalt concentrations were LOD at Stations 1 and 6 in the fall, while the highest (0.73 mg kg−1) were at Stations 2 and 7 in summer. There were no significant differences in Co concentrations in terms of sampling stations. There were significant seasonal differences between spring and summer, but not between fall and winter. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Co levels. The results showed that Co tended to bind the most to sediment particles of <0.063 mm.
Nickel (Ni): The lowest nickel concentration (106.92 mg kg−1) was at Station 7 in summer, while the highest was at Station 10 in summer (373.48 mg kg−1). Ni accumulation at Station 9 was significantly different from that at Stations 1, 2, and 4, as was Ni accumulation at Station 10 from Stations 1 and 4, and Ni element at Station 8 from Station 1 (p < 0.05). There were no statistically significant differences in seasonal accumulations.
Cadmium (Cd): The lowest cadmium concentrations (0.02 mg kg−1) were detected at Stations 5, 6, 7, and 10 in fall and at Station 1 in winter, while the highest (0.20 mg kg−1) were at Station 10 in spring season and Station 2 in fall. Cd sedimentary accumulation did not differ significantly between stations. However, Cd accumulation was significantly higher in summer than spring and fall. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Cd levels. The results indicated that Cd tends to bind the most to sediment particles of <0.063 mm.
Lead (Pb): The lowest lead concentration (11.02 mg kg−1) was at Station 2 in summer, while the highest (27.57 mg kg−1) was at Station 9 in spring. There were significant seasonal differences in PB sedimentary concentrations, which increased significantly in summer and winter, and decreased in spring and fall. There were also no significant differences according to sampling station. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Pb levels. The results showed that Pb tends to bind the most to sediment particles of <0.063 mm.
Zinc (Zn): The lowest zinc concentration (22.03 mg kg−1) was at Station 8 in fall, while the highest (87.17 mg kg−1) was at Station 1 in fall. While Zn concentrations increased in winter and accumulated in different parts of the lake in other seasons, these differences were not significant for either sampling stations or seasons. The core samples were segregated by sampling depth, divided into particle size subgroups using the PSA method, and analyzed for Zn levels. The results showed that Zn tends to bind the most to sediment particles of <0.063 mm. However, for the core sample taken from 5–10 cm depth, particles of >0.3 mm adsorbed as much Zn as particles of <0.063 mm.

3.2. Analyses of the Relationship between Total Organic Carbon (TOC) and Heavy Metal Concentrations

TOC values were determined in surface sediment samples taken from 10 different stations from Lake Bafa (Table 4).
The results show that Station 7 in the spring provided the highest TOC input to the surface sediment, while the lowest accumulation was at Station 4 (Table 4.). Seasonal TOC accumulation can be ranked from highest to lowest as follows: Spring > Summer > Fall > Winter.
There was a significant positive correlation between mean TOC values from the surface sediment and Cr accumulated in the sediment. In addition, there were significant positive correlations between Al and Ni levels, and Cd and Co levels (Table 5).
TOC levels in Lake Bafa sediment varied between 0.59 and 5.50 g kg−1. As a result of the correlation analyses between TOC and heavy metal concentrations in the Lake Bafa sediment, a positive correlation was determined between the amount of Cr and the amount of TOC. A strong positive correlation was also found between Al and Ni, and between Cd and Co.

3.3. Analyses of NIgeo, EF, CF, and PLI

NIgeo, EF, CF, and PLI were calculated for each heavy metal based on the sediment results and core samples.
For NIgeo, the results varied between <1 and 2–3 for the mean heavy metal concentrations in the surface sediment sampled from 10 different stations and the core samples from three different stations. For Al, Ni, and Mn, some stations were very slightly polluted, while all stations were very slightly polluted for Cr and Co. For Cd, some stations were slightly polluted and some stations were very slightly polluted (Table 6).
The enrichment factors calculated from the sediment sample data varied as follows for each heavy metal: EF(Al) 0.4–2.0; EF(Cr) 0.65–1.0; EF(Mn) 0.62–1.38; EF(Pb) 1.18–1.6; EF(Zn) 0.8–1.18; EF(Co) 0.6–1.1 and EF(Cd) 0.37–1.13. For all stations, the EF values indicated no anthropogenic enrichment (EF < 2) of heavy metals at most sites (Figure 2).
The CF results ranked the heavy metals from highest to lowest as follows: Al > Pb > Co > Zn > Cr > Mn > Cd. More specifically, the CF results indicate considerable Al, Pb, and Co contamination CF and moderate contamination for the remaining elements (Figure 3).
The PLI results from the present study indicated a significant pollution hazard in the lake (Figure 4).

3.4. Results of the Potential Ecological Risk Assessment (PERI)

Of the heavy metals examined in this study, the concentrations from the sediment samples for Cr and Ni were higher than both the TEC and PEC values from the SQGs (Table 7).
The indices were also used to assess the potential harm from heavy metal contamination (Table 8). Considering E r i , Cr, Mn, Pb, and Zn were categorized as low risk, while Cd was moderate risk. With regard to PERI, Stations 2, 8, 9, and 10 were categorized as moderate risk.

3.5. PCA

PCA was performed for all the environmental and water variables and the heavy metal sediment concentrations. The total variance was 75.392% (Figure 5). All the data used for this analysis is included in the supplementary files (see Supplementary File). The PCA showed that, in the first component, the most influential variables in the lake ecosystem were pH, temperature, and DO, while in the second component, they were Co, conductivity, Cd, TDS, and DO.

4. Discussion

This study reported the accumulation levels of nine heavy metals (Al, Cd, Co, Cr, Fe, Mn, Ni, Pb, and Zn) to assess the quality of the sediment in Lake Bafa, one of the most important wetlands in Turkey’s Aegean region. The lake’s sediment quality was examined in terms of TEC and PEC values, PERI, and NIgeo, EF, CF, and PLI indices. Lake Bafa was selected as the sampling area because of its unique aquatic ecosystem being connected to both the sea and the groundwater system. In addition, the lake is affected by several anthropogenic factors, particularly agricultural, domestic, and industrial waste. The most important problems facing the Büyük Menderes basin are the salinization of the land due to the rapidly developing industry, high rates of fertilizer use in agricultural areas, and excessive use of groundwater for irrigation [64]. Recent studies of the Büyük Menderes water quality define it as polluted [65,66,67,68].
The mean heavy metal concentrations in the surface sediment of Lake Bafa ranked as follows from highest to lowest: Fe > Al > Mn > Ni > Cr > Zn > Pb > Co > Cd. These results are consistent with those from previous studies of the same area [69,70] and other studies conducted in Turkey (Table 9). Considering each element separately, for example, Al sediment concentrations in the area where Büyük Menderes River enters Lake Bafa (Station 5) differed significantly from other stations. Similarly, previous research reported Al pollution in the Büyük Menderes River water column [68]. This suggests that Al pollution of Lake Bafa may be coming from the Büyük Menderes River. In this regard, it was thought that Lake Bafa might be polluted by the Büyük Menderes River in terms of the Al element. The statistical analyses indicated that Mn accumulated especially near olive oil factories (Station 9), while Ni values were highest in the surface sediment near olive oil factories (Station 9) and the hotel (Station 10). Previous studies showed that the liquid and solid wastes released during olive oil processing contained high Mn and Ni levels [71,72,73,74,75]. This suggested that the high Mn and Ni concentrations detected especially in these areas of Lake Bafa were caused by waste contaminated with these two elements from olive oil factories. Lake Bafa’s water budget is not known exactly and there is no definite information in the literature about underground water resources entering the lake from the lake floor. A previous study examined the groundwater quality parameters and heavy metal concentrations in the region of the Büyük Menderes River between Lake Bafa and the Aegean Sea coast [64]. The high Zn concentrations (134 mg L−1) were reported in groundwater taken from the station close to Lake Bafa. Thus, seasonal changes in Zn concentration within the lake may be due to groundwater.
As outlined earlier, the lake used to be a bay in the Aegean Sea before the expansion of the Büyük Menderes delta turned it into a lake, which implies that Lake Bafa can be regarded as a sediment trap. There is a continuous transport of sediment from the Büyük Menderes River to Lake Bafa. Considering that the heaviest rainfall in the area occurs during the fall and spring seasons, the hydrodynamic conditions of the Büyük Menderes River increase, making sediment transport more rapid, thereby causing more pollution due to heavy metal inflows. Of the various estimates made for Lake Bafa’s sedimentation rate [28,41,69], the mean value is 0.36 cm y−1. In other words, the core sample taken for the present study represents the last 30 years of accumulated sediment in the lake. The analysis showed that the core samples from 5–10 cm depth adsorbed more metal than those taken between 0–5 cm depth. However, the difference was not significant in terms of statistical analyses. It seemed that the pollution load pattern had been regular for a certain time.
TOC levels in Lake Bafa sediment varied between 0.59 and 5.50 g kg−1. In a previous study, similar mean TOC values (3.2 g kg−1) sampled from two different points were reported [82]. As a result of the correlation analyses between TOC and heavy metal concentrations in the Lake Bafa sediment, a positive correlation was determined between the amount of Cr and the amount of TOC. A strong positive correlation was also found between Al and Ni, and between Cd and Co. In a study of Lake Iznik, strong correlations were also reported between Al-Ni and Cd-Co levels in sediment samples [76].
Considering NIgeo for Ni and Mn, some stations were slightly polluted, while most stations were very slightly polluted for Cr and Co. For Cd, half of the stations were slightly polluted, while the other half were very slightly polluted. It was observed that in terms of Al, two stations where the Büyük Menderes River enters Bafa Lake and C1 core samples were very slightly polluted. Since there was a strong positive correlation between Al-Ni concentrations, it can be interpreted that Al might have a similar pollution potential in all stations in the future where Ni accumulates. In a similar study of Lake Bafa using NIgeo, almost all the surface and core sediments were categorized as unpolluted for Fe, Cr, Mn, Pb, Ni, Zn, and Cu, and lightly polluted for Hg [69]. In another previous study, it was found that some sediment samples taken from shallow areas of Lake Bafa were contaminated with Cd and Ni [70].
For all stations, the EF values indicated no anthropogenic enrichment (EF < 2) of heavy metals at most sites. The heavy metals with the highest EF values were Al and Pb, indicating that Al concentrations are affected by anthropogenic factors, while Pb concentrations are on the verge of being so. That is, the lake is being polluted with these two heavy metals. In addition, the correlation matrix indicates the lake’s sediment is at risk of Ni pollution in the coming years due to the strong positive relationship between Al and Ni levels. Consistent with previous studies for this area, the EF values did not indicate pollution in the lake sediment for the other tested metals. In parallel to the NIgeo and EF results, CF results indicate considerable Al, Pb, and Co contamination CF and moderate contamination for the remaining elements. That is, the lake is facing heavy metal pollution. A strong Cd contamination and moderate Ni contamination in certain locations of Lake Bafa were previously reported [70], while another study reported low Pb, Cu, Zn, and Mn contamination, and moderate Hg contamination at some stations [69].
The PLI results from the present study indicated a significant pollution hazard in the lake. The surface sediment was polluted by heavy metals, probably from anthropogenic sources. This sedimentary contamination may be caused by various factors: intense industrial activity in the Büyük Menderes region, domestic sewage outflow from settlements with no sewage infrastructure, waste from olive oil factories and tourist facilities, phosphate fertilizer and pesticide run off from agricultural areas, and heavy traffic on the Milas-Soke highway. Similar PLI findings from sampling points in Lake Bafa were also revealed in a previous study [70].
Of the heavy metals examined in this study, the concentrations from the sediment samples for Cr and Ni were higher than both the TEC and PEC values from the SQG’s. This indicates that Cr and Ni are likely to be having harmful effects on aquatic organisms in Lake Bafa. Although Ni is a naturally occurring element in the strata of the Büyük Menderes delta, where Lake Bafa is located [69,83], the excessive Ni concentrations cannot be of natural origin alone (both natural and anthropogenic sources). Although it is not bioaccumulated in natural ecosystems [84], the high surface sediment Ni concentrations may have anthropogenic sources, such as mining and mineral processing waste, emissions from fossil fuel vehicles, domestic and industrial waste, and organic and inorganic agricultural outputs [70,84,85]. Previous studies have also reported high Cr concentrations in Lake Bafa sediment [28,69,86]. The most important known pollutant sources of Cr are untreated domestic and industrial wastes [87], particularly leather industry wastewater originating from Uşak and Aydın Karacasu, which enters the lake via the Büyük Menderes River. The PERI results indicated that sampling stations in the east-southeast part of the lake, where the residential areas, tourism activities (hotel and restaurant area), and olive oil factories were located, and Station 2, which is the closest station to the Muğla Aydın highway, carried moderate risk. According to the PERI results, it can be said that the accumulation trend in the lake was in the southwest line. The PCA showed that, in the first component, the most influential variables in the lake ecosystem were pH, temperature, and DO, while in the second component, they were Co, conductivity, Cd, TDS, and DO.

5. Conclusions

The present study identifies the spatio-temporality of HMs in the sediments of Lake Bafa, Turkey. This study is one of the most comprehensive investigations using NIgeo, EF, CF, PLI, and PERI for Lake Bafa, which is one of the most important wetlands of Turkey’s Aegean region. By comparing the obtained findings with the results of the studies that have been done and will be done in wetlands with similar characteristics both in Turkey and in the world, meaningful scientific inferences can be made about the environmental fates of such toxic pollutants. Results revealed very significant results regarding the ecological sustainability of Lake Bafa. It was observed that the lake sediment has been under the pressure of heavy metal pollution. According to the results of the risk assessments, the concentrations of Al, Ni, Cr, Co, and Cd in the lake sediment may reach levels that will endanger the ecosystem in the future, and this accumulation is especially concentrated in the southwestern part of the lake. In addition, both in this study and in other similar previous studies, the accumulation of Al in the sediment was highlighted as very important, and it was emphasized that the most important source of this pollution was the Büyük Menderes River. For this reason, it is significant that both the local authorities take decisions in the short term, and the studies to be carried out in the long term should focus on preventing the accumulation of these heavy metals and their possible sources. To ensure the future sustainability of the lake’s ecosystem, it is important that such studies are conducted periodically to determine the lake’s ecological status, monitor changes in pollutant levels, and thereby provide early warning of future problems. In terms of practical implications, local and national authorities should always be aware of the lake’s ecological status and spare financial resources for scientific research in the area.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/2071-1050/15/13/9969/s1.

Author Contributions

Conceptualization, A.Y. and M.Y.; methodology, A.Y. and M.Y. software, A.Y.; validation, A.Y. and M.Y.; formal analysis, A.Y.; investigation, A.Y.; resources, A.Y.; data curation, A.Y.; writing—original draft preparation, A.Y.; writing—review and editing, A.Y.; visualization, A.Y.; supervision, M.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Muğla Sıtkı Koçman University Coordinatorship of Scientific Research (BAP 13/121) and Muğla Sıtkı Koçman University Coordinatorship of Teaching Staff Training Program (OYP). Also, it is based on the Ph.D. thesis of the corresponding author.

Data Availability Statement

Data for this study are available upon request to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jurdi, M.; Ibrahim Korfali, S.; Karahagopian, Y.; Davies, B.E. Evaluation of water quality of the Qaraaoun Reservoir, Lebanon: Suitability for multipurpose usage. Environ. Monit. Assess. 2002, 77, 11–30. [Google Scholar] [CrossRef] [PubMed]
  2. Mokhtar, M.B.; Toriman, M.E.H.; Hossain, M.; Abraham, A.; Tan, K.W. Institutional challenges for integrated river basin management in Langat River Basin, Malaysia. Water Environ. J. 2011, 25, 495–503. [Google Scholar] [CrossRef]
  3. Simsek, G. Urban River Rehabilitation as an Integrative Part of Sustainable Urban Water Systems. In Proceedings of the 48th International Society of City and Regional Planners Congress, Perm, Russia, 10–13 September 2012. [Google Scholar]
  4. Tunçer, S.; Uysal, H. İzmir ve Çandarlı körfezlerinde yaşayan bazı Mollusk türlerinde ağır metal kirlenmesi ile ilgili araştırmalar. Doğa Bilim Dergisi 1983, 12, 116–125. [Google Scholar]
  5. Curtis, H. Biology; Worth Publishers Inc.: New York, NY, USA, 1986; p. 992. [Google Scholar]
  6. Mason, C.F. Biology of Freshwater Pollution, 2nd ed.; John Wiley and Sons: New York, NY, USA, 1991; p. 351. [Google Scholar]
  7. Çukurçayır, F.; Geçer, C.; Arabacı, H. Yaşam için en değerli kaynaklar, hava ve su. TMM0B Meteoroloji Mühendisleri Odası Yayın Organı 1997, 2, 24–32. [Google Scholar]
  8. Murray, R.K.; Granner, D.K.; Mayes, P.A.; Radwell, V.W. Harper’in Biyokimyası, 24th ed.; Dikmen, N., Özgünen, T., Translators, Eds.; Barış Kitabevi: İstanbul, Turkey, 1996. [Google Scholar]
  9. Dodman, D. Environment and Urbanization. In The International Encyclopedia of Geography; John Wiley & Sons: Hoboken, NJ, USA, 2017. [Google Scholar]
  10. Egemen, Ö.; Sunlu, U. Su Kalitesi; Ege Üniv. Su Ürünleri Fak. Yayın.: İzmir, Turkey, 1999. [Google Scholar]
  11. Barlas, N. A pilot study of heavy metal concentration in various environments and fishes in the upper Sakarya River Basin, Turkey. Environ. Toxicol. 1999, 14, 367–373. [Google Scholar] [CrossRef]
  12. Sarıeyyüpoğlu, M.; Say, H. A study on heavy metal accumulation of Barbus capito pectoralis caught from the region of Elazığ sewage discharge into Keban Dam Lake. In Proceedings of the Ege University Faculty of Fisheries, Aquaculture Symposium, İzmir, Turkey, 12–14 November 1991; pp. 121–130. [Google Scholar]
  13. Wang, L.F.; Yang, L.Y.; Kong, L.H.; Li, S.; Zhu, J.R.; Wang, Y.Q. Spatial distribution, source identification and pollution assessment of metal content in the surface sediments of Nansi Lake, China. J. Geochem. Explor. 2014, 140, 87–95. [Google Scholar] [CrossRef]
  14. Priya, A.K.; Gnanasekaran, L.; Dutta, K.; Rajendran, S.; Balakrishnan, D.; Soto-Moscoso, M. Biosorption of heavy metals by microorganisms: Evaluation of different underlying mechanisms. Chemosphere 2022, 307, 135957. [Google Scholar] [CrossRef]
  15. Mason, C.F. Biology of Fresh Water Pollution; Longman: London, UK, 1996; pp. 265–267. [Google Scholar]
  16. Rainbow, P.S.; White, S.L. Comparative accumulation of cobalt by three crustaceans: A decapod, an amphipod and a barnacle. Aquat. Toxicol. 1990, 16, 113–126. [Google Scholar] [CrossRef]
  17. Ünlü, E.; Gümgüm, B. Concentrations of copper and zinc in fish and sediments from the Tigris River in Turkey. Chemosphere 1993, 26, 2055–2061. [Google Scholar] [CrossRef]
  18. Brezonik, P.K.; King, S.O.; Mach, C.E. The influence of water chemistry on trace metal bioavailability and toxicity in aquatic organisms. In Metal Ecotoxicology; Newman, M.D., McIntosh, A.W., Eds.; Lewis: Boca Raton, FL, USA, 1991. [Google Scholar]
  19. Nzeve, K.J. Assessment of Heavy Metal Contamination in Masinga Reservoir, Kenya. Ph.D. Thesis, Kenyatta University, Nairobi, Kenya, 2015. [Google Scholar]
  20. Chernoff, B.; Dooley, J.K. Heavy metals in relation to the biology of the mummichog Fundulus heteroctilus. J. Fish. Biol. 1979, 14, 309–328. [Google Scholar] [CrossRef]
  21. Eromesele, C.O.; Eromesele, I.C.; Muktar, S.L.M.; Birdling, S.A. Metals in fish from the upper Benue river and lakes Geriyo and Njuwa in northeastern Nigeria. Bull. Environ. Contam. Toxicol. 1995, 54, 8–14. [Google Scholar] [CrossRef] [PubMed]
  22. Matsui, S.; Barett, B.F.D.; Banerjee, J. Toxic substances management in lakes and reservoirs. In Guidelines of Lake Management; International Lake Environment Committee Foundation: Kusatsu, Japan, 1991; Volume 4. [Google Scholar]
  23. Adham, K.G.; Hassan, I.F.; Taha, N.; Amin, T.H. Impact of hazardous exposure to metals in the Nile and Delta lakes on the catfish, Clarias lazera. Environ. Monit. Assess. 1999, 54, 107–124. [Google Scholar] [CrossRef]
  24. Newman, M.C.; Unger, M.A. Fundamentals of Ecotoxicology, 2nd ed.; Lewis Publisher Press: Boca Raton, FL, USA, 2002. [Google Scholar]
  25. Cha, M.W.; Young, L.; Wong, K.M. The fate of traditional extensive (Gei Wai) shrimp farming at the Mai Po Marshes Nature Reserve, Hong Kong. Hydrobiologia 1997, 352, 295–303. [Google Scholar] [CrossRef]
  26. Tayab, M.R. Environmental Impact of Heavy Metal Pollution in Natural Aquatic Systems. Unpublished. Ph.D. Thesis, The University of West London, London, UK, 1991. [Google Scholar]
  27. Chukwujindu, M.A.; Godwin, E.N.; Francis, O.A. Assessment of contamination by heavy metals in sediments of Ase-River, Niger Delta, Nigeria. Res. J. Environ. Sci. 2007, 1, 220–228. [Google Scholar]
  28. Manav, R.; Uğur Görgün, A.; Filizok, I. Radionuclides (210Po and 210Pb) and some heavy metals in fish and sediments in Lake Bafa, Turkey, and the contribution of 210Po to the radiation dose. Int. J. Environ. Res. Public Health 2016, 13, 1113. [Google Scholar] [CrossRef]
  29. Soares, H.M.V.M.; Boaventura, R.A.R.; Machado, A.A.S.C.; Esteves da Silva, J.C.G. Sediments as monitors of heavy metal Contamination in Ave River basin (Portugal): Multivariate analysis of data. Environ. Pollut. 1999, 105, 311–323. [Google Scholar] [CrossRef]
  30. Shuhaimi, M.O. Metal concentration in the sediments of Richard Lake, Sudbury, Canada and sediment toxicity in an Ampipod Hyalella azteca. J. Environ. Sci. Technol. 2008, 1, 34–41. [Google Scholar] [CrossRef]
  31. Lamberson, J.O.; Dewitt, T.H.; Swartz, R.C. Assessment of sediment toxicity to marine benthos. In Sediment Toxicity Assessment; Buron, G.S., Ed.; Lewis: Boca Raton, FL, USA, 1992; pp. 183–211. [Google Scholar]
  32. Cook, N.H.; Wells, P.G. Toxicity of Halifax harbor sediments: An evaluation of Microtox Solid Phase test. Water Qual. Res. J. 1996, 31, 673–708. [Google Scholar] [CrossRef]
  33. Loizidou, M.; Haralambous, K.J.; Sakellarides, P.O. Environmental study of the marinas Part II. A study on the removal of metals from the Marianas sediment. Environ. Tech. 1992, 3, 245–252. [Google Scholar] [CrossRef]
  34. Magalhaes, C.; Coasta, J.; Teixeira, C.; Bordalo, A.A. Impacts of trace Metals on denitrification in estuarine sediments of the Douro River estuary, Portugal. Mar. Chem. 2007, 107, 332–341. [Google Scholar] [CrossRef]
  35. USEPA. Methods for Collection Storage and Manipulation of Sediments for Chemical and Toxicological Analyses: Technical Manual: EPA 823-B-01-002; U.S. Environmental Protection Agency, Office of Water: Washington, DC, USA, 2001. [Google Scholar]
  36. Baldwin, D.S.; Howitt, A.J. Baseline assessment of metals and hydrocarbons in the sediments of Lake Mulwala, Australia. Lakes Reserv. Res. Manag. 2007, 12, 167–174. [Google Scholar] [CrossRef]
  37. Abraha, G.A.; Mulu, B.D.; Yirgaalem, W.G. Bioaccumulation of heavy metals in fishes of Hashenge Lake, Tigray, Northern Highlands of Ethiopia. Am. J. Chem. 2012, 6, 326–334. [Google Scholar]
  38. Lijklema, L.; Koelmans, A.A.; Portielje, R. Water Quality Impacts of Sediment pollution and the Role of Early Diagnosis. Water Sci. Technol. 1993, 28, 1–16. [Google Scholar] [CrossRef]
  39. ATSDR (Agency for Toxic Substances and Disease Registry). Toxicological Profile for Chromium; U.S. Department of Health and Human Service, Public Health Service: Atlanta, GA, USA, 2000; pp. 95–134. [Google Scholar]
  40. Yabanlı, M.; Turk, N.; Tenekecioglu, E.; Uludag, R. Bafa Gölü’ndeki Toplu Balık Ölümleri Üzerine Bir Araştırma. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2011, 15, 36–40. [Google Scholar]
  41. Müllenhoff, M.; Handl, M.; Knipping, M.; Brückner, H. The evolution of Lake Bafa (Western Turkey)–Sedimentological, microfaunal and palynological results. Coastline Rep. 2004, 1, 55–66. [Google Scholar]
  42. Keser, B. Aydın ilinde Büyük Menderes nehri ile sulanan bölgelerde yetişen bazı sebze ve meyvelerdeki ağır metal kirliliğinin araştırılması. Unpublished. Master’s Thesis, Adnan Menderes University, Aydın, Turkey, 2008. [Google Scholar]
  43. Official Gazzette. Bakanlar Kurulu Kararı 94/5451-Sayı:21984. 1994. Available online: https://www.resmigazete.gov.tr/arsiv/21984.pdf (accessed on 12 April 2018).
  44. WWF. Available online: http://www.wwf.org.tr/ne_yapiyoruz/doga_koruma/doal_alanlar/buyuk_menderes_havzasi/ (accessed on 20 June 2017).
  45. Koljonen, T.; Darnley, A.G. The Geochemical Atlas of Finland, Part 2. Till. Econ. Geol. Bull. Soc. Econ. Geol. 1994, 89, 211. [Google Scholar]
  46. Lax, K.; Edén, P.; Björklund, A. Wide-spaced sampling of humus in Fennoscandia. J. Geochem. Explor. 1995, 55, 151–161. [Google Scholar] [CrossRef]
  47. Åström, M.; Björklund, A. Geochemistry and acidity of sulphide-bearing postglacial sediments of western Finland. Environ. Geochem. Health 1997, 19, 155–164. [Google Scholar] [CrossRef]
  48. Fortune, J. The Grainsize and Heavy Metal Content of Sediment in Darwin Harbour; Report No. 14/2006D; Aquatic Health Unit, Environmental Protection Agency, Northern Territory Government: Palmerston, NT, Australia, 2006. [Google Scholar]
  49. Gaudette, H.E.; Flight, W.R.; Toner, L.; Folger, D.W. An inexpensive titration method for the determination of organic carbon in recent sediments. J. Sediment. Res. 1974, 44, 249–253. [Google Scholar]
  50. Müller, G. Index of geoaccumulation in sediments of the Rhine River. GeoJournal 1969, 2, 108–118. [Google Scholar]
  51. Caeiro, S.; Costa, M.H.; Ramos, T.B.; Fernandes, F.; Silveira, N.; Coimbra, A.; Medeiros, G.; Painho, M. Assessing heavy metal contamination in Sado Estuary sediment: An index analysis approach. Ecol. Indic. 2005, 5, 151–169. [Google Scholar] [CrossRef]
  52. Buat-Menard, P. Influence de la Retombée Atmosphérique sur la Chimie des Métaux en Trace Dans la Matičre en Suspension de l’Atlantique Nord. Ph.D. Thesis, The University of Paris, Paris, France, 1979; p. 434. [Google Scholar]
  53. Kumar, V.; Sharma, A.; Minakshi, B.R.; Thukral, A.K. Temporal distribution, source apportionment, and pollution assessment of metals in the sediments of Beas River, India. Hum. Ecol. Risk Assess. 2018, 24, 2162–2181. [Google Scholar] [CrossRef]
  54. Kowalska, J.B.; Mazurek, R.; Gąsiorek, M.; Zaleski, T. Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination—A review. Environ. Geochem. Health 2018, 40, 2395–2420. [Google Scholar] [CrossRef]
  55. Zhang, L.; Ye, X.; Feng, H.; Jing, Y.; Ouyang, T.; Yu, X.; Liang, R.; Gao, C.; Chen, W. Heavy Metal Contamination in Western Xiamen Bay Sediments and its Vicinity, China. Mar. Pollut. Bull. 2007, 54, 974–982. [Google Scholar] [CrossRef] [PubMed]
  56. Hakanson, L. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
  57. Tomlinson, D.L.; Wilson, J.G.; Harris, C.R.; Jeffrey, D.W. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgol. Meeresunters 1980, 33, 566–575. [Google Scholar] [CrossRef]
  58. Abdullah, M.Z.; Louis, V.C.; Abas, M.T. Metal pollution and ecological risk assessment of Balok River sediment, Pahang Malaysia. Am. J. Environ. Eng. 2015, 5, 1–7. [Google Scholar]
  59. MacDonald, D.D.; Ingersoll, C.G.; Berger, T.A. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 2000, 39, 20–31. [Google Scholar] [CrossRef]
  60. Vivien, R.; Casado-Martínez, C.; Lafont, M.; Ferrari, B.J.D. Effect thresholds of metals in stream sediments based on in situ oligochaete communities. Environments 2020, 7, 31. [Google Scholar] [CrossRef]
  61. Mohajane, C.; Manjoro, M. Sediment-associated heavy metal contamination and potential ecological risk along an urban river in South Africa. Heliyon 2022, 8, e12499. [Google Scholar] [CrossRef]
  62. Decena, S.C.P.; Arguilles, M.S.; Robel, L.L. Assessing heavy metal contamination in surface sediments in an urban river in the Philippines. Pol. J. Environ. Stud. 2018, 27, 1983–1995. [Google Scholar] [CrossRef]
  63. Fang, X.; Peng, B.; Wang, X.; Song, Z.; Zhou, D.; Wang, Q.; Tan, C. Distribution, contamination and source identification of heavy metals in bed sediments from the lower reaches of the Xiangjiang River in Hunan province, China. Sci. Total Environ. 2019, 689, 557–570. [Google Scholar] [CrossRef]
  64. Somay, M.A.; Gemici, U. Groundwater quality degradation in the Buyuk Menderes River coastal wetland. Wat. Air Soil Poll. 2012, 223, 15–27. [Google Scholar] [CrossRef]
  65. Dağdelen, N.; Yeşilırmak, E.; Akçay, S.M.; Sezgin, F. Determination of water quality parameters of Buyuk Menderes River, Turkey. Asian J. Chem. 2009, 21, 287–298. [Google Scholar]
  66. Koç, C. A study on the pollution and water quality modeling of the River Buyuk Menderes, Turkey. Clean–Soil Air Water 2010, 38, 1169–1176. [Google Scholar] [CrossRef]
  67. Yeşilırmak, E.; Atatanır, L.; Yorulmaz, A.; Aydın, G.; Turgut, C. Spatial variability of Fe, Mn, Zn and Cu in soils of büyük menderes delta in western Turkey. Fresenius Environ. Bull. 2011, 20, 310–316. [Google Scholar]
  68. Adalı, Y.; Koca, Y.B. Effects of pollution on some tissues of fish collected from different regions of Büyük Menderes River: A histopathologıcal study. J. Environ. Prot. Ecol. 2016, 17, 477–487. [Google Scholar]
  69. Yılgör, S.; Kucuksezgin, F.; Ozel, E. Assessment of metal concentrations in sediments from Lake Bafa (Western Anatolia): An index analysis approach. Bull. Environ. Contam. Toxicol. 2012, 89, 512–518. [Google Scholar] [CrossRef]
  70. Algül, F.; Beyhan, M. Concentrations and sources of heavy metals in shallow sediments in Lake Bafa, Turkey. Sci. Rep. 2020, 10, 11782. [Google Scholar] [CrossRef] [PubMed]
  71. Komnitsas, K.; Zaharaki, D.; Doula, M.; Kavvadias, V. Origin of recalcitrant heavy metals present in olive mill wastewater evaporation ponds and nearby agricultural soils. Environ. Forensics 2011, 12, 319–326. [Google Scholar] [CrossRef]
  72. Curaqueo, G.; Schoebitz, M.; Borie, F.; Caravaca, F.; Roldán, A. Inoculation with arbuscular mycorrhizal fungi and addition of composted olive-mill waste enhance plant establishment and soil properties in the regeneration of a heavy metal-polluted environment. Environ. Sci. Pollut. Res. 2014, 21, 7403–7412. [Google Scholar] [CrossRef] [PubMed]
  73. Kavvadias, V.; Doula, M.; Theocharopoulos, S. Long-term effects on soil of the disposal of olive mill waste waters (OMW). Environ. Forensics 2014, 15, 37–51. [Google Scholar] [CrossRef]
  74. Hovorka, M.; Száková, J.; García-Sánchez, M.; Acebal, M.B.; García-Romera, I.; Tlustoš, P. Risk element sorption/desorption characteristics of dry olive residue: A technique for the potential immobilization of risk elements in contaminated soils. Environ. Sci. Pollut. Res. 2016, 23, 22614–22622. [Google Scholar] [CrossRef] [PubMed]
  75. Aharonov-Nadborny, R.; Tsechansky, L.; Raviv, M.; Graber, E.R. Impact of spreading olive mill waste water on agricultural soils for leaching of metal micronutrients and cations. Chemosphere 2017, 179, 213–221. [Google Scholar] [CrossRef] [PubMed]
  76. Ünlü, S.; Alpar, B. An assessment of trace element contamination in the freshwater sediments of Lake Iznik (NW Turkey). Environ. Earth Sci. 2016, 75, 140. [Google Scholar] [CrossRef]
  77. Tunca, E. Yeniçağa (Bolu) Gölü su, sediment ve tatlısu ıstakozu (Astacus leptodactylus, Eschscholtz, 1823)’nda bazı ağır metal birikimlerinin mevsimsel değişimi. Unpublished. Ph.D. Thesis, Ankara University, Ankara, Turkey, 2011. [Google Scholar]
  78. Başyiğit, B.; Tekin-Özan, S. Concentrations of Some Heavy Metals in Water, Sediment, and Tissues of Pikeperch (Sander lucioperca) from Karataş Lake Related to Physico-Chemical Parameters, Fish Size, and Seasons. Pol. J. Environ. Stud. 2013, 22, 633–644. [Google Scholar]
  79. Gülcü-Gür, B.; Tekin-Özan, S. The investigation of heavy metal levels in water and sediment from Işıklı Lake (Turkey) in relation to seasons and physico-chemical parameters. J. Aquac. Eng. Fish. Res. 2017, 3, 87–96. [Google Scholar] [CrossRef]
  80. Tekin-Özan, S. Determination of heavy metal levels in water, sediment and tissues of tench (Tinca tinca L., 1758) from Beyşehir Lake (Turkey). Environ. Monit. Assess. 2008, 145, 295–302. [Google Scholar] [CrossRef]
  81. Kır, İ.; Tekin-Özan, S.; Tuncay, Y. Kovada Gölü’nün su ve sedimentindeki bazı ağır metallerin mevsimsel değişimi. Ege Üniversitesi Su Ürünleri Dergisi 2007, 24, 155–158. [Google Scholar]
  82. Athina, P.; Simboura, N.; Stamatis, Z.; Akbulut, A.; Yaprak, E.A.; Beklioğlu, M. Su Kalitesi İzleme Ko-nusunda Kapasite Gelistirme Teknik Yardım Projesi Nihai Raporu; Proje no: TR2009/0327.02-02/001 2009; Mugla Sitki Kocman Universitesi Fen Fakultesi: Kötekli, Turkey, 2019. [Google Scholar]
  83. Akcay, H.; Oguz, A.; Karapire, C. Study of heavy metal pollution and speciation in Buyuk Menderes and Gediz River sediments. Water Res. 2003, 37, 813–822. [Google Scholar] [CrossRef]
  84. Wuana, R.A.; Okieimen, F.E. Heavy metals in contaminated soils: A review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol. 2011, 1–20. [Google Scholar] [CrossRef]
  85. Rathor, G.; Chopra, N.; Adhikari, T. Nickel as a pollutant and its management. Int. J. Environ. Res. 2014, 3, 94–98. [Google Scholar]
  86. Aydin-Onen, S.; Kucuksezgin, F.; Kocak, F.; Açik, S. Assessment of heavy metal contamination in Hediste diversicolor (O.F. Müller, 1776), Mugil cephalus (Linnaeus, 1758), and surface sediments of Bafa Lake (Eastern Aegean). Environ. Sci. Pollut. Res. 2015, 22, 8702–8718. [Google Scholar] [CrossRef] [PubMed]
  87. Al-Badaii, F.; Halim, A.A.; Shuhaimi-Othman, M. Evaluation of dissolved heavy metals in water of the Sungai Semenyih (Peninsular Malaysia) using environmetric methods. Sains. Malays. 2016, 45, 841–852. [Google Scholar]
Figure 1. Sediment and core (C) sampling station locations in Lake Bafa basin.
Figure 1. Sediment and core (C) sampling station locations in Lake Bafa basin.
Sustainability 15 09969 g001
Figure 2. Sediment enrichment factor plot. Sampling sites labelled from 1 to 12 represent Stations 1 to 10 and core samples 1 and 3, respectively.
Figure 2. Sediment enrichment factor plot. Sampling sites labelled from 1 to 12 represent Stations 1 to 10 and core samples 1 and 3, respectively.
Sustainability 15 09969 g002
Figure 3. Sediment contamination factor plot. Sampling sites are labelled from 1 to 12 to represent Stations 1 to 10 and core samples 1 and 3, respectively.
Figure 3. Sediment contamination factor plot. Sampling sites are labelled from 1 to 12 to represent Stations 1 to 10 and core samples 1 and 3, respectively.
Sustainability 15 09969 g003
Figure 4. PLI values for Bafa Lake sampling stations.
Figure 4. PLI values for Bafa Lake sampling stations.
Sustainability 15 09969 g004
Figure 5. PCA analysis results.
Figure 5. PCA analysis results.
Sustainability 15 09969 g005
Table 1. Sediment and core (C) sampling station locations and characteristics in Lake Bafa.
Table 1. Sediment and core (C) sampling station locations and characteristics in Lake Bafa.
StationCoordinatesFeatures/Depth (m)
Station 137°28′4″ N–27°27′37″ ERestaurant Location—4
Station 237°29′36″ N–27°24′59″ ENear Highway—21
Station 337°30′01″ N–27°23′56″ EOlive Tree Field 1—11
Station 437°30′30″ N–27°23′13″ ELake Bafa Stream Mouth and Fish Farm Location—8
Station 537°31′19″ N–27°24′08″ EBüyük Menderes Mouth and Serçin Village Location—3
Station 637°30′06″ N–27°26′35″ EMiddle of the Lake—18
Station 737°30′13″ N–27°27′11″ EMenet Isle—9
Station 837°30′06″ N–27°30′31″ EKapıkırı Village—5
Station 937°28′53″ N–27°31′15″ EOlive Tree Field 2—5
Station 1037°28′59″ N–27°28′27″ EHotel Location—7
C137°31′13″ N–27°24′06″ EBüyük Menderes Stream Mouth
C237°30′04″ N–27°26′52″ EMiddle of the Lake
C337°29′59″ N–27°30′34″ EEastern part of the Lake
Table 2. Detection limits for the Agilent 7700× ICP-MS.
Table 2. Detection limits for the Agilent 7700× ICP-MS.
Heavy MetalsSediment (µg kg−1)
Al0.127
Cd0.002
Co0.002
Cr0.036
Fe0.125
Mn0.037
Ni0.805
Pb0.121
Zn1.483
Table 3. Results of CRM016 Freshwater sediment certified trace metals reference material analysis (mg kg−1).
Table 3. Results of CRM016 Freshwater sediment certified trace metals reference material analysis (mg kg−1).
ElementCertified ValueMeasured ValueRecovery Rate (%)
Al8920 ± 6578110 ± 31890.92
Cd0.47 ± 0.080.43 ± 0.0591.49
Cr14.5 ± 1.3615.12 ± 1.02104.28
Fe16,800 ± 51715,093 ± 38989.84
Pb14.1 ± 0.6613.57 ± 0.2696.24
Mn180 ± 3.65166 ± 6.7492.22
Ni16.7 ± 0.5014.57 ± 0.3387.25
Zn69.7 ± 2.1172.88 ± 9.15104.56
Co5.96 ± 0.245.58 ± 0.3793.62
Table 4. The amount of TOC obtained from sediment samples taken from 10 Stations in Lake Bafa in four different seasons (g kg−1). In bold are the lowest and higher values.
Table 4. The amount of TOC obtained from sediment samples taken from 10 Stations in Lake Bafa in four different seasons (g kg−1). In bold are the lowest and higher values.
WinterSpringSummerFall
Station 11.683.152.582.52
Station 21.881.981.721.88
Station 31.811.963.181.49
Station 41.841.631.400.77
Station 52.552.580.593.06
Station 61.401.732.433.31
Station 71.735.503.071.74
Station 81.972.211.511.01
Station 92.461.832.442.38
Station 101.042.941.991.82
Table 5. Correlations between heavy metal concentrations and TOC in Lake Bafa sediment.
Table 5. Correlations between heavy metal concentrations and TOC in Lake Bafa sediment.
TOC
TOC1.00Al
Al−0.0201.00Cr
Cr0.370 *0.1951.00Fe
Fe−0.189−0.1310.1211.00Mn
Mg−0.0690.069−0.020−0.0751.00Ni
Ni−0.2210.847 **0.035−0.2120.1621.00Pb
Pb0.0110.2460.0870.1010.1740.1061.00Zn
Zn0.050−0.069−0.1780.256−0.118−0.1590.2621.00Cd
Cd−0.091−0.0550.006−0.0010.146−0.1160.1250.2321.00Co
Co−0.2170.213−0.0600.239−0.2740.033−0.1930.1030.505 **1.00
* p < 0.05, ** p < 0.01.
Table 6. NIgeo values for heavy metals detected in Lake Bafa sediment samples.
Table 6. NIgeo values for heavy metals detected in Lake Bafa sediment samples.
AlCrMnPbZnCoCdNi
Station 11–21–2<1<1<11–2<11–2
Station 2<11–21–2<1<11–21–21–2
Station 31–21–21–2<1<11–2<11–2
Station 4<11–21–2<1<11–2<11–2
Station 5<11–2<1<1<11–21–21–2
Station 6<11–2<1<1<11–2<1<1
Station 7<11–2<1<1<11–2<1<1
Station 8<11–2<1<1<11–21–2<1
Station 9<11–2<1<1<11–21–2<1
Station 10<11–21–2<1<11–21–2<1
C11–21–2<1<1<11–22–3-
C2<11–21–2<1<11–22–3-
C3<11–21–2<1<11–22–3-
Table 7. Heavy metal concentrations in sediment (mg−1 kg) and SQG a values.
Table 7. Heavy metal concentrations in sediment (mg−1 kg) and SQG a values.
AlCrMnPbZnCoCdNiFe
Station 15621.17209.79647.6814.3755.500.360.06239.1131,232.00
Station 24553.50214.63973.1514.8749.760.370.11210.5730,500.39
Station 35547.04225.66802.7415.4954.800.400.07222.9430,435.12
Station 44352.73219.90725.8714.0556.660.380.06229.8329,984.75
Station 53961.52234.70645.4513.9152.310.450.08173.0232,023.06
Station 62765.01218.84631.7715.7064.430.410.06120.4131,540.79
Station 73396.12263.87627.6114.3049.350.420.07114.0234,457.03
Station 82888.18227.39564.6214.3452.290.540.10112.1537,448.14
Station 93091.53235.09531.4116.8461.180.540.0936.6631,489.62
Station 101324.97231.25919.6213.6166.900.450.1152.5636,264.39
C17252.70379.44687.5512.7968.730.820.20-39,930.00
C23478.75364.34710.3212.8067.160.750.20-38,300.00
C34765.98357.60800.3312.8069.360.530.23-41,023.00
Avg.4076.86260.19712.9314.3059.110.490.11151.1334,202.18
TEC *-43.40-35.8121.00-0.9922.70-
PEC *-111.00-128.00459.00-4.9948.60-
TEC *: threshold effect concentration. PEC *: probable effect concentration. a MacDonald et al. (2000) [59].
Table 8. Ecological risk levels for each heavy metal ( E r i ) and potential ecological risk assessment (PERI) of sediment samples by sampling station.
Table 8. Ecological risk levels for each heavy metal ( E r i ) and potential ecological risk assessment (PERI) of sediment samples by sampling station.
CrMnPbZnCdPERIGrade of PERI
Station 14.312.1518.282.4845.0072.22low risk
Station 24.413.2318.922.2282.50111.28moderate risk
Station 34.632.6719.712.4552.5081.96low risk
Station 44.522.4117.882.5345.0072.33low risk
Station 54.822.1417.702.3460.0087.00low risk
Station 64.492.1019.972.8845.0074.45low risk
Station 75.422.0918.192.2052.5080.40low risk
Station 84.671.8818.242.3475.00102.13moderate risk
Station 94.831.7721.422.7367.5098.25moderate risk
Station 104.753.0617.322.9982.50110.61moderate risk
C12.501.075.431.0058.0068.00low risk
C32.280.965.211.0045.7555.21low risk
E r i Avg. 4.302.1316.522.2659.27
Grade of E r i low risklow risklow risklow riskmoderate risk
Table 9. Heavy metal values measured in sediment in similar studies carried out in Turkey (mg kg−1 dry weight).
Table 9. Heavy metal values measured in sediment in similar studies carried out in Turkey (mg kg−1 dry weight).
AlCdCoCrCuReferences
Lake İznik (Bursa)5.6 (%)0.2013.5063.7025.40[76]
Lake Yeniçağ-0.8-92.8–274.2-[77]
Lake Karataş (Burdur)-0.20-37.5621.95[78]
Lake Işıklı-0.09–0.30--5.12–16.48[79]
Lake Beyşehir (Konya)----7.16[80]
Lake Kovada (Isparta)6672.500.11-11.597.82[81]
Lake Bafa---181–38819.48–62.18[69]
Lake Bafa-0.18-259.20-[27]
Lake Bafa *--18.3588.9535.50[70]
Lake Bafa54.03–7251.380.02–0.20LOD-0.73LOD-284.50-This study
FeMnNiPbZnReferences
Lake İznik3.0 (%)791.4038.3017.6068.30[76]
Lake Yeniçağ-756–1143-8.4–16-[77]
Lake Karataş (Burdur)6968.62306.89131.810.8828.16[78]
Lake Işıklı1721.26–7700.27135.98–556.9511.55–38.930.91–5.5012.01–28.87[79]
Lake Beyşehir (Konya)10,390.59484.19--39.82[80]
Lake Kovada (Isparta)5030107.8515.872.9321.83[81]
Lake Bafa2.62–3.91 (%)625–1181153–5146.09–35.556.02–116.14[69]
Lake Bafa36,266.95703.08307.8017.2878.84[27]
Lake Bafa *32,850557.25195.00-42.05[70]
Lake Bafa2.23–4.13 (%)197.05–1331.47115.96–373.4811.02–27.5722.03–87.17This study
* Highest seasonal mean concentrations.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yozukmaz, A.; Yabanlı, M. Heavy Metal Contamination and Potential Ecological Risk Assessment in Sediments of Lake Bafa (Turkey). Sustainability 2023, 15, 9969. https://doi.org/10.3390/su15139969

AMA Style

Yozukmaz A, Yabanlı M. Heavy Metal Contamination and Potential Ecological Risk Assessment in Sediments of Lake Bafa (Turkey). Sustainability. 2023; 15(13):9969. https://doi.org/10.3390/su15139969

Chicago/Turabian Style

Yozukmaz, Aykut, and Murat Yabanlı. 2023. "Heavy Metal Contamination and Potential Ecological Risk Assessment in Sediments of Lake Bafa (Turkey)" Sustainability 15, no. 13: 9969. https://doi.org/10.3390/su15139969

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop