Content area
The present work studied the sources, concentrations, distributions, and possible ecotoxicological risks of trace metals (TMs) in surface sediments of the Kızılırmak and Yeşilırmak Rivers, the largest rivers in the Black Sea Region of Türkiye, and the Mert and Engiz Rivers located between these rivers, to evaluate the region’s health risks and pollution status. Average TM concentrations were measured in mg/kg and ordered from smallest to largest as Cd (4.1) < Co (19.9) < Ni (31.6) < Cu (34.9) < Pb (37.8) < Cr (197.6) < Zn (213.9) < Al (24,408.8) < Fe (35,920). Seasonal changes were observed, especially during the dry season, and Pb, Zn, and Co concentrations were found to increase. The geographic accumulation index (Igeo), contamination factor (CF), enrichment factor (EF), and pollution load index (PLI) used in environmental risk assessment indicate medium and high contamination levels and potential ecological effects. Similarly, while toxic risk index (TRI) and aggregate toxicity index (ATI) from toxicity assessment indices showed medium and high toxic levels, the highest individual growth rate (IGR) results of Hyallella azteca used in bioavailable TM assessment were observed in the control sediment (6.8), while the survival rate was 100% in the control sediment, it did not fall below 70% in the other sampling points. Bioaccumulation factor (BAF) results showed that Cu, Cd, Co, and Ni metals accumulated in H. azteca tissues. Health risk results indicated no health risk for adults while demonstrating a slight health risk for children. Pearson correlation coefficient (PCC) and principal component analysis (PCA) showed the presence of anthropogenic, lithogenic TM sources and slight effects of industrial and agrogenic sources.
Introduction
Coastal areas provide ecologically important services by forming a critical transition area between land and sea. These areas contain special species adapted to variable salinity levels and tidal movements, which contribute to the formation of high levels of biodiversity1, 2–3. However, since most of these areas are located close to settlements, they are severely affected by intense human activities. Approximately 40% of the world’s population lives within 100 km of the coast, while 22 of the 36 largest global cities are located in coastal areas4. This intense population pressure, combined with rapid urbanization and increased industrial activities, leads to serious environmental pollution, especially in coastal waters5,6.
Trace metal (TM) pollution has become a critical global environmental problem for aquatic environments7, 8, 9, 10, 11–12. The high bioaccumulation potential of trace metals, their high toxicity, their persistence in the environment, and their lack of biodegradability make them extremely dangerous for ecosystems and human health9,12, 13, 14, 15–16. TMs can enter the aquatic environment from natural sources (e.g., atmospheric deposition, soil erosion, and rock weathering) and anthropogenic factors such as industrial wastes, port and ship movements, untreated domestic wastewater, and agricultural activities17, 18, 19, 20, 21–22. Once in the water column, TMs are generally bound to suspended particles and settle over time, accumulating in sediments. Therefore, sediments are an important indicator reflecting the level of trace metal pollution in the long term and are a reservoir of pollution23, 24, 25–26.
To understand the ecological health of aquatic ecosystems and to develop effective management approaches, it is necessary to assess the extent of trace element pollution in surface sediments27, 28, 29–30. Since it is not practically possible to remove metal pollution from sediments, taking appropriate measures is one of the most effective methods25,31, 32–33.
Sediment Quality Guides (SQGs) set criteria to assess potential risks associated with metal pollution in river sediments27,34, 35–36. Various pollution indices such as geo-accumulation index (Igeo), enrichment factor (EF), contamination factor (CF) and pollution load index (PLI) are widely used to determine pollution levels. Ecological and toxicological risk assessments are made with indicators such as toxic risk index (TRI) and aggregate toxicity index (ATI)29,30,37, 38, 39, 40–41; these assessments are supported by statistical methods such as Principal Component Analysis (PCA) and Pearson correlation coefficient (PCC) to reveal pollution sources and relationships between elements. In addition, bioaccumulation studies using organisms such as Hyalella azteca reveal the effects of bioavailable metal forms and metrics such as bioaccumulation factor (BAF) and individual growth rate (IGR) provide important information in this process41, 42, 43–44.
Long-term exposure to trace elements in sediments may have significant adverse effects on human health. Therefore, the health risks that may arise from trace element exposure in sediments should not be ignored44. The numerous approaches mentioned can be valuable tools for understanding the current status of metal pollution, assessing the possible ecological risks of TMs and the possible effects of TMs in sediments on human health, and developing appropriate strategies to control pollution sources10,45.
Studies in the existing literature are often conducted using single methods or focus on specific water bodies such as lakes, wetlands, and dams. However, multifaceted and integrated approaches are needed to achieve a holistic understanding of pollution dynamics and associated ecological and health risks36,40,46.
This study aims to fill this gap by comprehensively assessing trace metal pollution in river sediments in the Middle Black Sea Region of Türkiye. The primary objective is to determine TM concentrations in sediments and to reveal ecological risk levels by means of (Igeo, EF, CF, PLI) indices. The secondary objective is to evaluate toxicological risks using TRI and ATI indices, to examine the effects on H. azteca together with bioaccumulation factor (BAF) and individual growth rate (IGR) and to consider all these data holistically within the framework of health risk assessment. TM sources are determined and spatial-seasonal distributions are revealed by using statistical methods such as principal component analysis (PCA) and pearson correlation coefficient (PCC). In this context, the study aims to provide valuable information for the protection and sustainable management of aquatic ecosystems in Türkiye.
Material and methods
Study area and sampling
Samsun, one of the thirty provinces with metropolitan status in Türkiye, was selected as the study area. Samples were collected from four sampling points, namely Kızılırmak (R1,D1), Engiz (R2,D2), Mert (R3,D3) and Yeşilırmak (R4,D4), located between the two largest rivers of the Black Sea Region, Kızılırmak and Yeşilırmak, in February 2019 (rainy (R) season) and in July 2019 (dry (D) season) (supplementary material Fig S1). Specimens were collected using a Birge-Ekman grab sampler at a 0–10 cm depth, and about 3–4 m from the river bank. The sampler tool was washed with pure water before each sampling to prevent contamination. All samples were sent to the laboratory in acid-washed sterile plastic containers and stored in a refrigerator at + 4 °C for further analysis.
The sampling points were selected as representative points to determine the sediment quality in residential areas and agricultural and animal husbandry activities and are located in the Middle Black Sea Region, in the center of the northernmost part of Türkiye. According to the address-based population registration system of TUIK, the population is 1,377,546 people as of 202347. The total surface area of Samsun is 9,725 km2, of which 45% is mountains, 37% is plateaus and 18% is plains, respectively. In addition to intensive agricultural and animal husbandry activities in the region, beekeeping and fishing activities are also carried out48.
Sediment and H. azteca metal analyses
In the laboratory, sediment specimens were mixed in a homogeneous manner, and allowed to dry in the oven at a temperature of 103 °C for a period of 24 h. Afterward, the dried sediments were sieved using 63 μm mesh sieve. Sediment specimens were digested before elemental analysis to make them suitable for analysis. The method used by41 was applied for digestion. A Perkin Elmer Optima 7000 DV Inductively Coupled Plasma/Optical Emission Spectrometry (ICP-OES) instrument was utilized to conduct the elemental analysis of sediment specimens (Fe, Co, Pb, Cu, Cd, Cr, Al, Ni, and Zn). Analyses were performed in triplicate. BCR 667 reference material (Institute for Reference Materials and Measurements, IRMM; European Commission Joint Research Centre) was utilized for quality/control and the analytical method’s accuracy. Recoveries were above 90% of the certified values.
A 10-day toxicity test was carried out with H. azteca with the objective for evaluating the accumulation of TMs in living tissues ecotoxicologically43. Accordingly, for the amphipod toxicity test, 300 ml glass jars were prepared at 23 ± 1 °C, under 16 L:8D (16 h of light: 8 h of dark) conditions, one of which was control, at a 1:4 ratio with three replicates, 2 cm sediment, and 8 cm surface water. According to49, the endpoints were survival and growth after 10 days. A white tray and a 300 µm sieve were used to remove the amphipods from the jar. After the amphipods remaining in the sieve were removed, the remaining ones were removed from the tray with the help of micropipettes. Since there were 10 H. azteca in each test jar, the remaining amphipods were identified and noted. When the experiment ended, 10 H. azteca Falcon tubes were checked at the beginning of the experiment and put into the freezer to calculate the growth rate. Then, all tubes were taken out of the freezer. After all tests were completed, the living creatures that we put in the Eppendorf tube and took out of the freezer were dried in a freeze dryer for 24 h by opening a hole in the tube and weighed with a precision of ± 0.0001 to determine their dry weight. Table S1 (supplementary material) contains the weighing results of the toxicity test endpoints.
For the digestion, 200 µl of 67% HNO3 was added to amphipods, and after 24 h, it was kept in a 100-W microwave oven for 2 min. Then, 50 µl of H2O2 was added, and after 30 min, it was kept in a 300-W microwave oven for 1 min. The previous process was repeated three times, to ensure complete digestion of amphipods, and the final volumes were completed to 5 ml with ultrapure water. ICP-MS was utilized to determine the specimens’ metal concentrations. SRM 2976 reference material was utilized as a reference. The reference material’s recovery rates were 75.88–141.36% for Cd, Pb, Cu, and Zn. However, certified values are not available for Cr and Ni.
Sediment quality guidelines (SQGs)
SQGs represent threshold concentrations of pollutants in sediments, aiming to protect benthic organisms in the sediment against negative impacts50, 51, 52, 53–54. There are SQGs of various pollutants, such as trace metals and organic pollutants, for pollution management and risk assessment10,55, 56, 57, 58–59. Therefore, the environmental risks of TMs were evaluated by comparing the contents of TMs with SQGs. Threshold effect concentration (TEC) and probable effect concentration (PEC), defined as probable effect level (PEL), specific effect level (SEL), and threshold effect level (TEL), are the indices in SQGs33. Table 1 lists TEC, PEC, TEL, PEL, and SEL values for TMs.
Table. 1. Comparison of TMs in Samsun river sediments with average shale values and sediment quality guidelines (unit mg/kg).
Rivers | Cd | Pb | Ni | Fe | Zn | Cu | Al | Cr | Co | |
|---|---|---|---|---|---|---|---|---|---|---|
Rainy season | R1 | 4.1 | 16.0 | 59.0 | 22,630.0 | 138.0 | 35.0 | 16,470.0 | 240.0 | 11.0 |
R2 | 4.0 | 19.0 | 17.0 | 39,550.0 | 186.0 | 43.0 | 32,280.0 | 207.0 | 18.0 | |
R3 | 3.0 | 22.0 | 11.0 | 16,970.0 | 134.0 | 28.0 | 12,100.0 | 165.0 | 10.0 | |
R4 | 4.9 | 38.0 | 39.0 | 30,940.0 | 175.0 | 38.0 | 31,700.0 | 205.0 | 16.0 | |
Dry season | D1 | 5.0 | 60.0 | 19.0 | 43,770.0 | 234.0 | 31.0 | 39,740.0 | 169.0 | 27.0 |
D2 | 4.0 | 49.0 | 69.0 | 41,000.0 | 195.0 | 36.0 | 12,110.0 | 247.0 | 28.0 | |
D3 | 4.1 | 42.0 | 20.0 | 42,410.0 | 459.0 | 38.0 | 19,680.0 | 176.0 | 25.0 | |
D4 | 3.9 | 56.0 | 19.0 | 50,090.0 | 190.0 | 30.0 | 31,190.0 | 172.0 | 24.0 | |
Mean | 4.1 | 37.8 | 31.6 | 35,920.0 | 213.9 | 34.9 | 24,408.8 | 197.6 | 19.9 | |
Average shale values | 0.3 | 20.0 | 68.0 | 47,200.0 | 95.0 | 45.0 | - | 90.0 | 19.0 | |
SQGs | ||||||||||
TEC | 0.99 | 35.8 | 22.7 | - | 121 | 31.6 | - | 43.4 | - | |
PEC | 4.98 | 128 | 48.6 | - | 459 | 149 | - | 111 | - | |
TEL | 0.6 | 35 | 18 | - | 123 | 35.7 | - | 37.3 | - | |
PEL | 3.53 | 91.3 | 36 | - | 315 | 197 | - | 90 | - | |
SEL | 10 | 250 | 75 | - | 820 | 110 | - | 110 | - |
Sediment contamination and environmental risk assessment indices (EF, CF, Igeo, and PLI)
Enrichment factor (EF), geoaccumulation index (Igeo), contamination factor (CF), and pollution load index (PLI) were computed with the objective of determining the pollution levels of trace metals in sediments. PLI shows the sum pollution degree of TMs and their combined risks to the benthic ecosystem. At the same time, EF, Igeo, and CF indicate the pollution degree of a certain TM and its impact on the benthic ecosystem separately25,26,60,61. The world average shale values were accepted as background (or pre-industrial) values for TMs because local geochemical background contents of TMs are absent54 (Table 1). Table S2 in supplementary material, presents the formula of the mentioned indices, definitions of the formulae, and classes of the indices.
Sediment and H. azteca toxicity assessment indices (TRI, ATI, IGR, and BAF)
The toxic risk index (TRI) evaluated the ecosystem’s toxicity15,62. SQG was used to calculate the TRI value, which includes the probable effect level (PEL) and the threshold effect level (TEL). The aggregate toxicity index (ATI), introduced by64, was used with the objective of assessing the toxicity degree of TMs in sediments64. ATI, developed on the basis of separate toxic factors for every metal, was computed according to the relative effect range median (ERM) values of metals suggested by65. Individual growth rate (IGR) was calculated to determine toxicity on H. azteca exposed to TMs in sediments59. BAF assessed the transfer of TMs from sediments to living tissues (H. azteca). Diverse BAFs among organisms show their capability to accumulate TMs from the environment40,60.
Table S2 contains the formulae used in the calculation of TRI, ATI, IGR, and BAF used in sediment and H. azteca toxicity assessment, the coefficients used in the calculation, and the definitions of the rating.
Human health risk assessment (HHRA)
HHRA is examined in two different categories, adults and children, with the objective of estimating the carcinogenic and non-carcinogenic impacts of TMs66, 67, 68, 69–70. In general, contaminants can enter the human body via three potential routes: dermal, ingestion, and inhalation. This work researched the dermal and ingestion modes71,72. Only lead, chromium, and cadmium were included in carcinogenic risk (CR) assessments. In addition to carcinogenic concerns, the research also calculated hazard indices (HIs) in order to evaluate the non-carcinogenic risks (hazard quotients: HQs) related to all TMs in the sediment. The health risk indices were determined on the basis of the equations suggested by73,74 accepted the threshold value of CR as 1 × 10−4, while the admissible range was presented as 1 × 10−6 to 1 × 10−4. The USEPA RSL calculator75was used to confirm all HQs, HI, and CR results.
Carcinogenic and non-carcinogenic risks were computed with the equations below (1), (2), (3), (4), (5), (6), and (7):
Non-carcinogenic risks for recreational receptors:
1
2
Carcinogenic risks for recreational receptors:
3
where:4
where:Additionally, total carcinogenic risk (TCR) values and total non-carcinogenic risks (hazard index (HI), total HI (THI)) were calculated. HI, THI, and TCR values were calculated using the formulae below:
5
6
7
Supplementary material Tables S3 and S4 list descriptions, units, and values for all equations used in HHRA calculations.
Data analysis
Before data analysis, the Kolmogorov–Smirnov test was conducted to first verify data for normality64,76. The independent samples t-test and one-way analysis of variance (ANOVA) determined spatio-temporal differences. Pearson correlation coefficient (PCC) was employed in order to identify the degree of correlation of TMs in sediment specimens. Principal component analysis (PCA) determined possible relationships between specific elements and contamination sources. Kaiser–Meyer–Olkin (KMO value > 0.5) and Bartlett’s sphericity tests (p < 0.001) investigated the validity and applicability of PCA results72,77,78. Statistical analyses were conducted in software tools, such as Origin 2022 and SPSS® 22.
Results and discussion
Distribution of trace metals in sediments
Table 1 contains the TM concentrations measured in the Kızılırmak, Engiz, Mert and Yeşilırmak rivers located in Samsun, Middle Black Sea Region, during the dry and rainy seasons, average shale values61 and limit values in SQGs34. The concentration ranges of TMs were found in mg/kg as 3.0–5.0, 16.0–56.0, 11.0–69.0, 16,970.0–50,090.0, 134.0–459.0, 28.0–43.0, 12,110.0–39,740.0, 165.0–247.0, and 11.0–28.0 for Cd, Pb, Ni, Fe, Zn, Cu, Al, Cr, and Co, respectively. Cd, Zn, and Cr values were above the average shale values at all sampling points. When the total TM contents of the rivers are examined in Fig. 1, it is seen that they differ according to the rivers in the rainy and dry seasons. The lowest TM concentration (29,443.0 mg/kg) was observed at sampling point R3 during the rainy season, while it was observed in sampling point D2 during the dry season with a value of 53,738.0 mg/kg. According to the total TM concentration in the rainy and dry seasons, TM pollution of the rivers followed the following order: R2 > R4 > R1 > R3 during the rainy season and D1 > D4 > D3 > D2 during the dry season (Fig. 1). The high concentrations of Al and Fe in sampling points R2 and R1, which were high in both seasons, indicate that they were affected by different lithological units40,79.
Fig.1 [Images not available. See PDF.]
Total metal contents of TMs in Samsun river sediments (unit mg/kg).
Compared with the average shale values in Table 1, Cd, Zn, and Cr were above the limit values at all sampling points. Ni was above the limit value only at sampling point D2 during the dry season and remained below the limit value of 68 mg/kg at all other sampling points. High Cd, Zn and Cr concentrations at all sampling points suggest that the study area is affected by anthropogenic sources25,64,80. Upon comparing the findings for the rainy season with those for the dry season, it is seen that TM concentrations increased. While the limit value of 19 mg/kg for Co was not exceeded in the rainy season, it was exceeded in all sampling points of dry season. While temporal differences in TM concentrations demonstrate seasonal or periodic alterations affected by human activities, natural processes, and weather conditions, spatial differences can indicate specific pollution sources30,36,81,82. According to the TEC and PEC values in SQG, the PEC limit value is not exceeded in both dry and rainy seasons except for Ni and Cr. Considering the TEC limit values, Cd, Zn, and Cr are exceeded at all sampling points in a similar way to the average shale and TEL values. Regarding PEL limit values are examined, it is seen that Cd and Cr exceeded the limit values during both seasons and at all sampling points. In terms of SEL, only Cr was above the limit value (110 mg/kg) at all sampling points. The average mg/kg concentrations of TMs in rainy and dry seasons are as follows: Fe (35,920 ± 10,630.39) > Al (24,408.8 ± 9901.39) > Zn (213.9 ± 97.37) > Cr (197.6 ± 30.38) > Pb (37.8 ± 15.99) > Cu (34.9 ± 4.65) > Ni (31.6 ± 4.65) > Co (19.9 ± 6.66) > Cd (4.1 ± 0.57).
Environmental risk assessment
Fig. 2 presents EF, CF and Igeo values and their averages, while supplementary material Table S5 contains index calculations for seasons and sampling points, including PLI. When the average EF values in the dry and rainy seasons were examined, the enrichment degrees were shown as Cd (15.18) > Zn (3.07) > Pb (2.75) > Cr (2.29) > Co (1.47) > Cu (0.81) > Ni (0.51) in the dry season and Cd (24.28) > Cr (4.22) > Zn (3.02) < Pb (2.19) > Cu (1.44) > Co (1.27) < Ni (0.86) in the rainy season. While Ni, Cu, and Co displayed minimum enrichment below 2 in both periods, Pb, Zn, and Cr displayed moderate enrichment, and Cd showed significant enrichment during the dry season and very high enrichment in the rainy season.
Fig.2 [Images not available. See PDF.]
a) EF, b) CF, c) Igeo values, and averages of sampling points according to rainy and dry seasons.
In case of EF > 1.5, there is the highest possibility that enrichment is attributed to anthropogenic sources; metals varying between 0.05 and 1.50 are classified as lithogenic in origin30,36,72. Accordingly, during both dry and rainy seasons, the sources of Ni, Cu, and Co metals can be considered lithogenic, while the sources of other metals can be considered anthropogenic.
Concerning Igeo values, Cd showed a heavily polluted value between 3 and 4 at all sampling points. It had a moderate to heavily polluted degree with a value of 2.74 only in the rainy season (Table S5). Igeo values below 0 demonstrate the absence of pollution in sediments83. Similar to EF at all sampling points, Ni, Cu, and Co values below 0 indicate that the area is not polluted with these metals, Pb (negative) shows that the area is unpolluted in the rainy season and moderately polluted in the dry season. Zn and Cr have a moderately polluted pollution degree in both seasons.
If CF < 1, it is estimated that there is low contamination regarding at the beginning. Accordingly, Cu showed low contamination in both seasons, while Co showed low contamination during the rainy season (0.73) and moderate contamination during the dry season (1.36). While the average CF values showed a moderate contamination degree for all metals except for Cd, Cd showed very high contamination during the rainy (13.33) and dry (14.16) seasons.
The pollution load index (PLI) evaluates the degree of correlation between TMs and surface sediments, which can possibly affect the sediment’s fauna and microflora72,84. The present work employed PLI to identify the general sediment pollution level at the sampling points. In case of PLI > 1, high contamination status was observed at all sampling points in both seasons (supplementary material Table S2). Very high contamination was observed for Cd metal, parallel to CF, which was reflected in the PLI results. When seasonal means were examined, values of 1.71 for the rainy season and 2.23 for the dry season showed high contamination for PLI. Likewise, in the research by24, sampling points showed high contamination at PLI > 1 in all seasons due to high CF values for Cd and Pb metals.
Toxicological risk assessment
TRI, ATI indices, IGR, and BAF revealed the toxic effects of TMs in the study area. Furthermore, the survival rate of H. azteca after exposure to sediments was investigated. Figure 3 shows TRI and ATI values for the rainy and dry seasons.
Fig. 3 [Images not available. See PDF.]
TRI and ATI results in the rainy and dry seasons.
TRI aims to provide data on the cumulative toxic risk of all quantitative metals collected in the study area for benthic life25,39,71. The results in Fig. 3 show a moderate toxic risk degree at all sampling points during both seasons, except point R3 (9.31) during the rainy season, and a considerable toxic risk degree only at sampling point D2 (15.86) during the dry season.
In line with the ATI results developed by63 according to the toxic factor and SQG for each metal separately, R3 (0.41) and R2 (0.50) were at a moderate toxic level in the rainy season, while other sampling points showed a high toxic level. Apart from these, D2 (1.05) was at an extremely toxic level in the dry season, as in TRI (Fig. 3).
As seen in Fig. 4, IGR had the highest value (6.8) in the control sediment, while it had the lowest individual growth rate with a value of 1.13 at sampling point R2. Upon evaluating the survival rate, no significant difference was revealed between the control and contaminated sediments, and the survival rate did not fall below 70%.
Fig. 4 [Images not available. See PDF.]
Individual growth rate (IGR) and survival rate after the sediment exposure of H. azteca.
66 performed an environmental risk assessment against high Zn release in sediment pore water, and the toxicity test results for H. azteca and Daphnia magna determined that the mean survival for D. magna was 95.4 ± 2.4%, IGR 17.8 ± 1.7 μgday −1, while the survival rate for H. azteca never fell below 85%. It was reported that high Zn concentrations were negatively correlated with H. azteca IGR and that Zn release affected IGR. Likewise, in our study, IGR decreased at sampling points R2 (186 mg/kg) and R4 (175 mg/kg), showing the highest Zn concentration in the rainy season.
According to the BAF results in Fig. 5, BAF < 1 value was observed in all control specimens. Cu, Cd, Co, and Ni metals were determined to accumulate in H. azteca tissues at other sampling points. The highest accumulation for Cu was observed at sampling point R1 (11.65), for Cd and Co at sampling point R4 (4.66 and 6.11, respectively), and for Ni at sampling point R2 (4.92).
Fig. 5 [Images not available. See PDF.]
10-day metal bioaccumulation (BAF) results of H. azteca toxicity test.
Bioaccumulation factors (BAFs) are employed in risk assessment with the objective of estimating the trophic transition of pollutants including TMs from sediments and can help estimate the risks related to the said transition41,85. Accordingly, the research by86 evaluated the potential of the amphipod Synurella ambulans as a bioindicator of metal pollution in the Sava River and reported that metals accumulated in the amphipod in the order presented as, Cu > Cd > Cr > Co > Rb > Ni. In S. ambulans, when it comes to the six metals in question (Cr, Cd, Ni, Cu, Rb, and Pb) known to have toxic impacts on biota, it would be appropriate to use BAFs for hazard identification86, 87, 88, 89–90. However, since bioaccumulation processes are different and bioaccumulation patterns differ among species and are specific for every metal91,92, the metal accumulation order in our study was Cu > Co > Ni > Cd16. evaluated metal accumulation in macroalgae in Mersin coastal sediments and reported that among Co, Al, Cd, Cu, Cr, Mn, Fe, Ni, Zn, and Pb metals, Cd > Al > Zn > Cu accumulation was higher than the other metals. Differences in metal pollution, bioaccumulation processes, and bioindicator species showed that species accumulate metals, but there may be changes in BAF levels; however, they can be used for hazard identification. Accordingly, there is a spatial change in metal concentrations, and the bioaccumulation of metals is area- and species-specific93.
Human health risk assessment
Non-carcinogenic and carcinogenic health risk assessments related to TMs in sediments were performed for children and adults, taking into account both the dermal and ingestion contact exposure routes. These measurements are crucial for assessing and communicating possible health outcomes related to exposure to TMs in sediment specimens11. They provide a comprehensive summary, helping make conscious decisions and effectively manage environmental issues94,95. Table 2 contains the computed HQ, HI, and THI values for adults and children and the LCR values for adults. The findings demonstrated that individual metals did not cause any detrimental health effects on adults through the ingestion and dermal contact routes. However, for children, the THQ (1.5) and THI values (HI ingestion + HI dermal) (1.53) for the ingestion route were a little bit higher than 1, showing that metals in the sediment may pose a slight health risk to children because of their combined or interactive impacts10.
Table 2. Carcinogenic (LCR and TLCR) and non-carcinogenic (HQ, HI, and THI) risks for recreational use.
Adult | Child | Adult | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
HQing | HQderm | HI | HQing | HQderm | HI | LCRing | LCRderm | TLCR | ||
Rainy | Cd | 2.11E-03 | 3.57E-04 | 2.47E-03 | 1.97E-02 | 1.87E-03 | 2.16E-02 | 5.06E-05 | 5.79E-06 | 5.64E-05 |
Pb | 8.98E-03 | 3.79E-05 | 9.02E-03 | 8.38E-02 | 1.99E-04 | 8.40E-02 | 6.83E-08 | 1.95E-10 | 6.85E-08 | |
Ni | 8.32E-04 | 8.78E-05 | 9.20E-04 | 7.77E-03 | 4.61E-04 | 8.23E-03 | ||||
Fe | 2.08E-02 | 8.77E-05 | 2.09E-02 | 1.94E-01 | 4.60E-04 | 1.94E-01 | ||||
Zn | 2.79E-04 | 1.18E-06 | 2.80E-04 | 2.60E-03 | 6.18E-06 | 2.61E-03 | ||||
Cu | 4.76E-04 | 2.01E-06 | 4.78E-04 | 4.44E-03 | 1.05E-05 | 4.45E-03 | ||||
Cr | 7.20E-05 | 2.34E-05 | 9.53E-05 | 6.72E-04 | 1.23E-04 | 7.94E-04 | 2.05E-04 | 4.51E-05 | 2.50E-04 | |
Co | 2.43E-02 | 1.03E-04 | 2.44E-02 | 2.27E-01 | 5.38E-04 | 2.27E-01 | ||||
Dry | Cd | 2.27E-03 | 3.84E-03 | 6.11E-03 | 2.12E-02 | 2.01E-02 | 4.13E-02 | 5.44E-05 | 6.22E-05 | 1.17E-04 |
Pb | 1.95E-02 | 8.25E-05 | 1.96E-02 | 1.82E-01 | 4.33E-04 | 1.83E-01 | 8.84E-07 | 2.53E-09 | 8.87E-07 | |
Ni | 8.40E-04 | 8.87E-05 | 9.29E-04 | 7.84E-03 | 4.65E-04 | 8.31E-03 | ||||
Fe | 3.35E-02 | 1.41E-04 | 3.36E-02 | 3.12E-01 | 7.41E-04 | 3.13E-01 | ||||
Zn | 4.75E-04 | 2.00E-06 | 4.77E-04 | 4.43E-03 | 1.05E-05 | 4.44E-03 | ||||
Cu | 4.46E-04 | 1.89E-06 | 4.48E-04 | 4.17E-03 | 9.89E-06 | 4.18E-03 | ||||
Cr | 6.73E-05 | 2.19E-05 | 8.91E-05 | 6.28E-04 | 1.15E-04 | 7.43E-04 | 1.92E-04 | 4.22E-05 | 2.34E-04 | |
Co | 4.58E-02 | 1.93E-04 | 4.60E-02 | 4.27E-01 | 1.01E-03 | 4.28E-01 | ||||
THI | 1.66E-01 | THI | 1.53E + 00 | |||||||
When the carcinogenic health risk assessment Cd, Pb, and Cr values were computed for adults, they were ranked as Cr (2.50E-04) > Cd (5.64E-05) > Pb (6.85E-08) in the rainy season and similarly as Cr (2.34E-04) > Cd (1.17E-04) > Pb (8.87E-07) during the dry season. All calculated LCR values were lower than the target risk threshold of 1.00E-04 (1 × 10–4), indicating an insignificant carcinogenic risk for adults engaged in recreational activities in the study area.
Determination of estimated sources of TMs
Pearson correlation coefficient (Fig. 6a) and PCA (Fig. 6b) determined the possible sources of TMs in river sediments. Previous research indicated that TMs with strong correlations arise from the identical source25,59,96.
Fig. 6 [Images not available. See PDF.]
Graphical representation of (a) PCC, (b) scree plot and rotation matrix PCA.
As seen in Fig. 6a, Cr-Ni and Co-Fe (r > 0.90) were very strongly positively correlated, while Pb-Fe, Al–Pb, Co-Pb, and Al-Co were strongly positively correlated (r > 0.80). According to the PCA results in Fig. 6b and supplementary material Table S6, 94.17% of the total variance is explained by four factors with eigenvalues greater than 1. Co, Fe, Pb, and Zn represent the first component PC1, with a contribution of 42.21%. These results suggested that a common contamination source for these TMs was of natural/lithogenic origin39. The second component, PC2, explains 24.76% of the total variance and consists of Ni and Cr parameters, displaying a strong positive correlation (r = 0.91). The strong correlation among these metals is predominantly related to anthropogenic sources, highlighting the considerable effect of human activities on their presence in sediments. The third component, PC3, explains 14.92% of the total variance and is represented by Al and Cd. This factor can be explained by agrogenic contributions from various agricultural activities and water–rock relations resulting from chemical weathering78,97,98. The last component, PC4, explains 12.27% of the total variance and is represented by Cu metal. It can be said that Cu (supplementary material Table S6) with a positive loading value > 0.92 is of industrial origin.
Conclusion
This study assessed the TM contamination of Samsun sediments in the Middle Black Sea Region using various analytical methods and a health risk index and determined the ecotoxic effects on H. azteca in an integrated manner for the first time.
The average concentrations of PTEs in sediments, given in mg/kg, are presented in decreasing order as follows: Fe (35,920 ± 10,630.39) > Al (24,408.8 ± 9901.39) > Zn (213.9 ± 97.37) > Cr (197.6 ± 30.38) > Pb (37.8 ± 15.99) > Cu (34.9 ± 4.65) > Ni (31.6 ± 4.65) > Co (19.9 ± 6.66) > Cd (4.1 ± 0.57). The SQG assessment results show that some metals are exceeded by the middle shale (Cd, Pb, Zn, Cr, Co), TEC (all metals), PEC (Cr), TEL (all metals except Cu), and PEL (Cd, Cr), SEL (Cr), and the content of these TMs may negatively impact benthic organisms in the sediment.
According to EF, CF, Igeo, and PLI indices, TMs displayed medium and high eco-environmental risks; TRI and ATI indices showed medium and high eco-toxicological risks, while sampling point D2 showed an extremely toxic level according to both indices. According to IGR, the highest individual growth rate was in the control sediment, and the survival rate did not fall below 70%. According to BAF results, Cu, Cd, Co, and Ni metals accumulated in H. azteca tissues. Non-carcinogenic risks (THI) did not have harmful effects on adults, while it was determined that they might cause health risks, albeit low, in children. Carcinogenic risks (TCR) were within or below the risk range. PCC and PCA results found four sources of metals in the sediment: natural (lithogenic), anthropogenic, agrogenic, and low-level industrial sources.
Based on these results, TMs from various sources pollute the Middle Black Sea sediments, which highlights the risks to sediment ecosystems and the potential health hazards, especially for children. Future studies should include continuous monitoring and implementation of necessary management strategies to prevent these hazards and risks.
Acknowledgements
The author is grateful to Dr. Fikret Ustaoğlu for his contributions to the creation of the figures. Special thanks are extended to the esteemed editor and anonymous reviewers for their constructive comments and suggestions to improve this manuscript.
Author contributions
AŞ, collected sediment specimens, performed the laboratory analysis of the specimens, completed the data analysis and interpretation, prepared the draft and finalized it.
Data availability
The author declares that the data will be provided upon request.
Declaration
Competing of interests
The authors declare no competing interests.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-13663-3.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
1. Birch, GF. A review and critical assessment of sedimentary metal indices used in determining the magnitude of anthropogenic change in coastal environments. Sci. Total Environ.; 2023; 854, 1:CAS:528:DC%2BB38XisVGls7rO [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.158129] 158129.
2. Islam, MSMM; Bhattacharjee, SC; Fahim, KM; Islam, MA; Sarkar, MSU; Alam, M et al. Mapping trace metal footprints: Distribution, sources, and risk assessment in coastal sediments near a heavy industrial zone in Bangladesh. Mar. Pollut. Bull.; 2025; 211, 1:CAS:528:DC%2BB2cXislejsLfM [DOI: https://dx.doi.org/10.1016/j.marpolbul.2024.117405] 117405.
3. Perna, PV; Febbraro, MD; Carranza, ML; Marzialetti, F; Innangi, M. Remote sensing and invasive plants in coastal ecosystems: Whatwe know So far and future prospects. Land; 2023; 12, 341. [DOI: https://dx.doi.org/10.3390/land12020341]
4. Liu, X; Yu, S. Anthropogenic metal loads in nearshore sediment along the coast of China mainland interacting with provincial socioeconomics in the period 1980–2020. Sci. Total Environ.; 2022; 839, 1:CAS:528:DC%2BB38XhsFyhtrnL [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.156286] 156286.
5. Liu, T; Zhang, Y; Liu, Y; Chen, B. Multi-indicator assessment of heavy metal pollution in Qinzhou Harbour sediments: Unraveling ecological and human health risks. Mar. Pollut. Bull.; 2025; 211, 1:CAS:528:DC%2BB2cXivVSgsrjK [DOI: https://dx.doi.org/10.1016/j.marpolbul.2024.117442] 117442.
6. Varol, M; Ustaoğlu, F; Tokatlı, C. Metal pollution, eco-health risks and source apportionment in coastal sediments of Samsun, Türkiye: A receiving zone for the Kızılırmak and Yeşilırmak rivers. Environ Res.; 2025; 9,
7. Li, S; Zhang, Q. Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques. J. Hazard Mater.; 2010; 176, pp. 579-588.1:CAS:528:DC%2BC3cXpsVCmtg%3D%3D
8. Bing, HJ; Zhou, J; Wu, YH; Wang, X; Sun, H; Li, R. Current state, sources, and potential risk of heavy metals in sediments of Three Gorges Reservoir. China. Environ. Pollut.; 2016; 214, pp. 485-496.1:CAS:528:DC%2BC28XntlSju7g%3D
9. Ustaoglu, F; Tepe, Y; Aydin, H. Heavy metals in sediments of two nearby streams from Southeastern Black Sea coast: Contamination and ecological risk assessment. Environ. Forensics; 2020; 21, pp. 145-156.1:CAS:528:DC%2BB3cXnsV2ksrc%3D
10. Varol, M. Environmental, ecological and health risks of trace metals in sediments of a large reservoir on the Euphrates River (Turkey). Environ. Res.; 2020; 187, 1:CAS:528:DC%2BB3cXhtVaqtLfK [DOI: https://dx.doi.org/10.1016/j.envres.2020.109664] 109664.
11. Yüksel, B; Ustaoglu, F; Tokatli, C; Islam, MS. Ecotoxicological risk assessment for sediments of Çavus¸lu stream in Giresun, Turkey: Association between garbage disposal facility and metallic accumulation. Environ. Sci. Pollut. Res.; 2022; 29,
12. Saha, N; Rahman, MS; Ahmed, MB; Zhou, JL; Ngo, HH; Guo, W. Industrial metal pollution in water and probabilistic assessment of human health risk. J. Environ. Manag.; 2017; 185, pp. 70-78.1:CAS:528:DC%2BC28XhslKjs7vP
13. Qu, L; Huang, H; Xia, F; Liu, Y; Dahlgren, RA; Zhang, M. Risk analysis of heavy metal concentration in surface waters across the rural-urban interface of the WenRui Tang River. China. Environ. Pollut.; 2018; 237, pp. 639-649.1:CAS:528:DC%2BC1cXktlGktbk%3D
14. Tian, K; Wu, Q; Liu, P; Hu, W; Huang, B; Shi, B; Zhou, Y; Kwon, BO; Choi, K; Ryu, J; Khim, JS; Wang, T. Ecological risk assessment of heavy metals in sediments and water from the coastal areas of the Bohai Sea and the Yellow Sea. Environ. Int.; 2020; 136, 1:CAS:528:DC%2BB3cXhvVehtr8%3D 105512.
15. Varol, M; Ustaoglu, F; Tokatlı, C. Ecological risk assessment of metals in sediments from three stagnant water bodies in Northern Turkey. Curr. Pollut. Rep.; 2022; 8,
16. Sharma A, Grewal AS, Sharma D, Srivastav AL Heavy metal contamination in water: consequences on human health and environment. Metals in Water. Elsevier 39–52 (2023)
17. Karadede-Akin, H; Ünlü, E. Heavy metal concentrations in water, sediment, fish and some benthic organisms from Tigris River. Turkey. Environ. Monit. Assess.; 2007; 131, pp. 323-337.1:CAS:528:DC%2BD2sXmvVaqu7Y%3D
18. Varol, M; Sen, B. Assessment of nutrient and heavy metal contamination in surface water and sediments of the upper Tigris River, Turkey. CATENA; 2012; 92, pp. 1-10.1:CAS:528:DC%2BC38Xjt1Gjs7Y%3D
19. Xu, F; Liu, Z; Cao, Y; Qiu, L; Feng, J; Xu, F; Tian, X. Assessment of heavy metal contamination in urban river sediments in the Jiaozhou Bay catchment, Qingdao, China. CATENA; 2017; 150, pp. 9-16.1:CAS:528:DC%2BC28XhvVemur%2FP
20. Zhu, L; Liu, J; Xu, S; Xie, Z. Deposition behavior, risk assessment and source identification of heavy metals in reservoir sediments of Northeast China. Ecotoxicol. Environ. Saf.; 2017; 142, pp. 454-463.1:CAS:528:DC%2BC2sXotlOqu74%3D
21. Al-Kahtany, K; El-Sorogy, AS; Alharbi, T; Giacobbe, S; Nour, HE. Health risk assessment and contamination of potentially toxic elements in southwest of the red sea coastal sediment. Reg. Stud. Mar. Sci.; 2023; 65, 103103.
22. Akçay I Environmental risk assessment and bioaccumulation of heavy metals in sediments and macroalgae (Enteromorpha sp.) along the Mersin Coast (Türkiye), Northeastern Mediterranean Sea, Regional Studies in Marine Science 77 103673. https://doi.org/10.1016/j.rsma.2024.1036737 (2024)
23. Gu, X; Han, X; Han, Y; Luo, W; Feng, M; Xu, D; Xing, P; Wu, QL. Sedimentary records and stable lead isotopes reveal increasing anthropogenic impacts on heavy metal accumulation in a plateau lake of China over the last 100 years. J. Hazard Mater.; 2022; 440, 1:CAS:528:DC%2BB38Xit1Gqur7E 129860.
24. Aydın, H; Tepe, Y; Ustaoglu, F. A holistic approach to the eco-geochemical risk assessment of trace elements in the estuarine sediments of the Southeastern Black Sea. Mar. Pollut. Bull.; 2023; 189, 1:CAS:528:DC%2BB3sXjvVKnsLo%3D [DOI: https://dx.doi.org/10.1016/j.marpolbul.2023.114732] 114732.
25. Islam, ARMT; Varol, M; Habib, MA; Khan, R. Risk assessment and source apportionment for metals in sediments of Kaptai Lake in Bangladesh using individual and synergistic indices and a receptor model. Mar. Pollut. Bull.; 2023; 190, 114845.1:CAS:528:DC%2BB3sXmtFWmsLg%3D [DOI: https://dx.doi.org/10.1016/j.marpolbul.2023.114845]
26. Tokatlı, C; Varol, M; Ustaoglu, F; Muhammad, S. Pollution characteristics, sources and health risks assessment of potentially hazardous elements in sediments of ten ponds in the Saros Bay region (Türkiye). Chemosphere; 2023; 340, 139977.1:CAS:528:DC%2BB3sXhvVSkurjK [DOI: https://dx.doi.org/10.1016/j.chemosphere.2023.139977]
27. Haghnazar, H; Hudson-Edwards, KA; Kumar, V; Pourakbar, M; Mahdavianpour, M; Aghayani, E. Potentially toxic elements contamination in surface sediment and indigenous aquatic macrophytes of the Bahmanshir River, Iran: Appraisal of phytoremediation capability. Chemosphere; 2021; 285, 1:CAS:528:DC%2BB3MXhsFegu77E [DOI: https://dx.doi.org/10.1016/j.chemosphere.2021.131446] 131446.
28. Jaskuła, J; Sojka, M; Fiedler, M; Wrozynski, R. Analysis of spatial variability of river bottom sediment pollution with heavy metals and assessment of potential ecological hazard for the Warta river Poland. Minerals; 2021; 11,
29. Jaskula, J; Sojka, M. Assessment of spatial distribution of sediment contamination with heavy metals in the two biggest rivers in Poland. CATENA; 2022; 211, 1:CAS:528:DC%2BB3MXivVCjtrzJ [DOI: https://dx.doi.org/10.1016/j.catena.2021.105959] 105959.
30. Tepe, Y; Şimşek, A; Ustaoglu, F; Tas, B. Spatial–temporal distribution and pollution indices of heavy metals in the Turnasuyu Stream sediment Turkey. Environ. Monit. Assessment; 2022; 194,
31. Muhammad, S. Evaluation of heavy metals in water and sediments, pollution, and risk indices of Naltar Lakes Pakistan. Environ. Sci. Pollut. Res.; 2023; 30, pp. 28217-28226.1:CAS:528:DC%2BB38XjtVWit7%2FI
32. El-Magd, SAA; Taha, TH; Pienaar, HH; Breil, P; Amer, RA; Namour, P. Assessing heavy metal pollution hazard in sediments of Lake Mariout. Egypt. J. Afr. Earth Sci.; 2021; 176, 104116.
33. Li, D; Yu, R; Chen, J; Leng, X; Zhao, D; Ji, H; An, S. Ecological risk of heavy metals in lake sediments of China: A national scale integrated analysis. J. Clean. Prod.; 2022; 334, 1:CAS:528:DC%2BB38XitV2jtbk%3D 130206.
34. MacDonald, DD; Ingersoll, CG; Berger, TA. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol.; 2000; 39, pp. 20-31.1:CAS:528:DC%2BD3cXjvFCkurw%3D [DOI: https://dx.doi.org/10.1007/s002440010075]
35. Kukrer, S; Tunc, IO; Erginal, AE; Bay, O; Kılıç, S. Distribution, sources and ecological risk assessment of metals in Kura River sediments along a human disturbance gradient. Environ. Forensic; 2022; 23,
36. Ustaoğlu, F; Yüksel, B; Tepe, Y; Aydın, H; Topaldemir, H. Metal pollution assessment in the surface sediments of a river system in Türkiye: Integrating toxicological risk assessment and source identification. Mar. Pollut. Bull.; 2024; 203, 116514.1:CAS:528:DC%2BB2cXhtFGqt7rI [DOI: https://dx.doi.org/10.1016/j.marpolbul.2024.116514]
37. Ma, X; Zuo, H; Tian, M; Zhang, L; Meng, J; Zhou, X; Min, N; Chang, X; Liu, Y. Assessment of heavy metals contamination in sediments from three adjacent regions of the Yellow River using metal chemical fractions and multivariate analysis techniques. Chemosphere; 2016; 144, pp. 264-272.2016Chmsp.144.264M1:CAS:528:DC%2BC2MXhsVyrt7rO [DOI: https://dx.doi.org/10.1016/j.chemosphere.2015.08.026]
38. Fural, S; Kükrer, S; Cürebal, I; Aykır, D. Ecological degradation and noncarcinogenic health risks of potential toxic elements: A GIS-based spatial analysis for Dogancı Dam (Turkey). Environ. Monit. Assess.; 2022; 194, 269.1:CAS:528:DC%2BB38XnslCgtr4%3D [DOI: https://dx.doi.org/10.1007/s10661-022-09870-4]
39. Şimşek, A; Ozkoç, HB; Bakan, G. Environmental, ecological and human health risk assessment of heavy metals in sediments at Samsun-Tekkekoy, North of Turkey. Environ. Sci. Pollut. Res.; 2022; 29, pp. 2009-2023.1:CAS:528:DC%2BB38XhsFyrurw%3D [DOI: https://dx.doi.org/10.1007/s11356-021-15746-w]
40. Muhammad, S; Zeb, A; Shaik, MR; Assal, ME. Spatial distribution of potentially toxic elements pollution and ecotoxicological risk of sediments in the high-altitude lakes ecosystem. Phys. Chem. Earth.; 2024; 135, pp. 103655-103661. [DOI: https://dx.doi.org/10.1016/j.pce.2024.103655]
41. Şimşek, A; Teuchies, J; Haghnazar, H; Blust, R; Bakan, G. Evaluation of bioaccumulation and toxicity of Tubifex tubifex exposed to contaminated river sediment by potentially toxic elements- A case study of the Middle Black Sea Turkey. J Explor Geochem; 2023; [DOI: https://dx.doi.org/10.1016/j.gexplo.2023.107263]
42. Borgmann, U; Ralph, KM; Norwood, WP. Toxicity test procedures for Hyalella azteca, and chronic toxicity of cadmium and pentachlorophenol to H. azteca, Gammarus fasciatus and Daphnia magna. Arch Environ Contam Toxicol; 1989; 18, pp. 756-764.1:CAS:528:DyaL1MXls1yrtbs%3D
43. Ingersoll, CG; Ankley, GT; Benoit, DA; Brunson, EL; Burton, GA; Dwyer, FJ; Hoke, RA; Landrum, PF; Norberg-King, TJ; Winger, PV. Toxicity and bioaccumulation of sediment-associated contaminants using freshwater invertebrates: A review of methods and applications. Environ Toxicol Chem; 1995; 14, pp. 1885-1894.1:CAS:528:DyaK2MXovFylsr0%3D
44. Bartlett, AJ; Struger, J; Grapentine, LC; Palace, VP. Examining impacts of current-use pesticides in Southern Ontario using in situ exposures of the amphipod Hyalella azteca. Environ Toxicol Chem.; 2016; 35,
45. Kormoker, T; Karmoker, R; Uddin, M; Kabir, MH; Siddique, MAB; Khan, R; Islam, MS; Al, MA; Alam, M; Ustaoglu, F; Islam, M; Idris, AM. Toxic elemental abundances in the sediment of the Jamuna River, Bangladesh: Pollution status, sources, toxicity, and ecological risks assessment. Int. J. Environ. Anal. Chem.; 2022; [DOI: https://dx.doi.org/10.1080/03067319.2022.2134781]
46. Domingo, JPT; Ngwenya, BT; Attal, M; David, CPC; Mudd, SM. Geochemical fingerprinting to determine sediment source contribution and improve contamination assessment in mining-impacted floodplains in the Philippines. Appl. Geochem.; 2023; 159, 1:CAS:528:DC%2BB3sXit1Wqt77J [DOI: https://dx.doi.org/10.1016/j.apgeochem.2023.105808] 105808.
47. TUIK (2023) TUIK Data Portal For Statistics.https://data.tuik.gov.tr/Kategori/GetKategori?p=nufus-ve-demografi-109&dil=1
48. Üstün Odabaşı, S; Ceylan, Z. Water quality and health risk assessment of potentially toxic elements in water of Samsun Rivers of the Mid-Black Sea. Turkey. Environ Earth Sci; 2023; 82, 501.2023EES..82.501U1:CAS:528:DC%2BB3sXitVeitLbM [DOI: https://dx.doi.org/10.1007/s12665-023-11189-3]
49. USEPA Methods for Measuring the Toxicity and Bioaccumulation of Sediment-Associated Contaminants with Freshwater Invertebrates, Second Edition. EPA/600/R-94/024, United States Environmental Protection Agency, Washington, DC. 2000
50. Batley GE, Stahl RG, Babut MP, Bott TL, Clark JR, Field LJ, Ho K, Mount DR, Swartz RC, Tessier A, Wenning R, Batley G, Ingersoll C, Moore D The scientific underpinnings of sediment quality guidelines. Use of Sediment Quality Guidelines and Related Tools For the Assessment of Contaminated Sediment. SETAC Press, Pensacola, FL, USA 2005
51. Burton, GA. Assessing sediment toxicity: Past, present, and future. Environ. Toxicol. Chem.; 2013; 32, pp. 1438-1440.1:CAS:528:DC%2BC3sXptlOisrc%3D
52. Burton, GA. Breaking from tradition: Establishing more realistic sediment quality guidelines. Environ. Sci. Pollut. Res.; 2018; 25, pp. 3047-3052.1:CAS:528:DC%2BC2sXkslOksw%3D%3D
53. Kwok, KWH; Batley, GE; Wenning, RJ; Zhu, LY; Vangheluwe, M; Lee, S. Sediment quality guidelines: Challenges and opportunities for improving sediment management. Environ. Sci. Pollut. Res.; 2014; 21, pp. 17-27.
54. Proshad, R; Kormoker, T; Al, MA; Islam, MS; Khadka, S; Idris, AM. Receptor model-based source apportionment and ecological risk of metals in sediments of an urban river in Bangladesh. J. Hazard. Mater.; 2022; 423, 1:CAS:528:DC%2BB3MXhvFGjtb3K 127030.
55. Long, ER; Ingersoll, CG; Macdonald, DD. Calculation and uses of mean sediment quality guideline quotients: A critical review. Environ. Sci. Technol.; 2006; 40, pp. 1726-1736.1:CAS:528:DC%2BD28XhtFWjtrY%3D
56. Meador, JP. Rationale and procedures for using the tissue-residue approach for toxicity assessment and determination of tissue, water, and sediment quality guidelines for aquatic organisms. Hum. Ecol. Risk Assess.; 2006; 12, pp. 1018-1073.1:CAS:528:DC%2BD2sXmvVSqtQ%3D%3D
57. Burgess, RM; Berry, WJ; Mount, DR; Di Toro, DM. Mechanistic sediment quality guidelines based on contaminant bioavailability: equilibrium partitioning sediment benchmarks. Environ. Toxicol. Chem.; 2013; 32, pp. 102-114.1:CAS:528:DC%2BC3sXhtFSrs7w%3D
58. Simpson S, Batley G Sediment Quality Assessment: A Practical Guide, 2nd ed. CSIRO Publishing. 2016
59. Varol, M; Ustaoglu, F; Tokatlı, C. Ecological risks and controlling factors of trace elements in sediments of dam lakes in the Black Sea Region (Turkey). Environ. Res.; 2022; 205, 1:CAS:528:DC%2BB3MXis1Kgsr3J [DOI: https://dx.doi.org/10.1016/j.envres.2021.112478] 112478.
60. Liu, X; Zhang, L; Zhang, L. Concentration, risk assessment, and source identification of heavy metals in surface sediments in Yinghai: A shellfish cultivation zone in Jiaozhou Bay China. Mar. Pollut. Bull.; 2017; 121, pp. 216-221.1:CAS:528:DC%2BC2sXps1Gkurc%3D [DOI: https://dx.doi.org/10.1016/j.marpolbul.2017.05.063]
61. Turekian, KK; Wedepohl, KH. Distribution of the elements in some major units of the earth’s crust. Geol. Soc. Am. Bull.; 1961; 72,
62. Ustaoglu, F; Kükrer, S; Tas, B; Topaldemir, H. Evaluation of metal accumulation in Terme River sediments using ecological indices and a bioindicator species. Environ. Sci. Pollut. Res.; 2022; 29,
63. Jamshidi-Zanjani, A. Saeedi M (2017) Multivariate analysis and geochemical approach for assessment of metal pollution state in sediment cores. Environ. Sci. Pollut. Control Ser.; 2017; 24,
64. Haghnazar, H; Belmont, P; Johannesson, KH; Aghayani, E; Mehraein, M. Human-induced pollution and toxicity of river sediment by potentially toxic elements (PTEs) and accumulation in a paddy soil-rice system: A comprehensive watershed-scale assessment. Chemosphere; 2023; 311,
65. Long, ER; Macdonald, DD; Smith, SL; Calder, FD. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environ. Manage.; 1995; 19, pp. 81-97.1995EnMan.19..81L
66. Nedrich, SM; Burton, GA. Sediment Zn-release during post-drought re-flooding: Assessing environmental risk to Hyalella azteca and Daphnia magna. Environ Pollut.; 2017; 230, pp. 1116-1124.1:CAS:528:DC%2BC2sXht12ht73N [DOI: https://dx.doi.org/10.1016/j.envpol.2017.07.073S]
67. USEPA (2020a) Exposure Assessment Tools by Media- Water and Sediment. https://www.epa.gov/expobox/exposure-assessment-tools-media-water-and-sediment (Accessed 10 September 2024).
68. Du, Q; Wu, J; Xu, F; Yang, Y; Li, F. Occurrence, species, and health effects of groundwater Arsenic in typical rural areas along the northern foot of the Qinling Mountains China. Expo Heal; 2023; [DOI: https://dx.doi.org/10.1007/S12403-023-00576]
69. Guo, W; Li, P; Du, Q; Zhou, Y; Xu, D; Zhang, Z. Hydrogeochemical processes regulating the groundwater Geochemistry and human health risk of groundwater in the rural areas of the Wei river Basin China. Expo Heal; 2023; [DOI: https://dx.doi.org/10.1007/S12403-023-00555-Y]
70. Ravindiran, G; Janardhan, G; Rajamanickam, S; Sivarethinamohan, S; Murali, V; Hayder, G. Study on hydrogeochemical assessment, groundwater quality index for drinking, seawater mixing index and human health risk assessment of nitrate and fluoride. Groundw. Sustain. Dev.; 2024; 25, 101161.
71. Topaldemir, H; Tas, B; Yüksel, B; Ustaoglu, F. Potentially hazardous elements in sediments and Ceratophyllum demersum: An ecotoxicological risk assessment in Miliç Wetland, Samsun Türkiye. Environ. Sci. Pollut. Res.; 2023; 30,
72. Yüksel, B; Ustaoglu, F; Aydın, H; Tokatlı, C; Topaldemir, H; Islam, MdS; Muhammad, S. Appraisal of metallic accumulation in the surface sediment of a fish breeding dam in Türkiye: A stochastical approach to ecotoxicological risk assessment. Mar. Pollut. Bull.; 2024; [DOI: https://dx.doi.org/10.1016/j.marpolbul.2024.116488]
73. USEPA (2020b) Regional Screening Levels (RSLs) – Equations. https://www.epa.gov/r isk/regional-screening-levels-rsls-equations (Accessed 10 September 2024)
74. USEPA Risk Assessment Guidance for Superfund, Volume I: Human Health Evaluation Manual (Part B, Development of Risk-Based Preliminary Remediation Goals). Office of Emergency and Remedial Response. EPA/540/R-92/003. (1991)
75. USEPA (2020c) RSL Calculator. https://epa-prgs.ornl.gov/cgi-bin/chemicals/csl_search (Accessed 10 September 2024).
76. Mehraein, M; Torabi, M; Sangsefidi, Y; MacVicar, B. Numerical simulation of free flow through side orifice in a circular open-channel using response surface method. Flow Meas. Instrum.; 2020; 76, 101825.
77. Elhaik, E. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Sci. Rep.; 2022; 121,
78. Muhammad, S; Ullah, I. Spatial and temporal distribution of heavy metals pollution and risk indices in surface sediments of Gomal Zam Dam Basin. Pakistan. Environ. Monit. Assess.; 2023; 195,
79. Varol, M; Canpolat, O; Eris, KK; Çaglar, M. Trace metals in core sediments from a deep lake in eastern Turkey: vertical concentration profiles, eco-environmental risks and possible sources. Ecotoxicol. Environ. Saf.; 2020; 189, 1:CAS:528:DC%2BC1MXitlOgsbbO 110060.
80. Rezapour, S; Asadzadeh, F; Nouri, A; Khodaverdiloo, H; Heidari, M. Distribution, source apportionment, and risk analysis of heavy metals in river sediments of the Urmia Lake basin. Sci Rep.; 2022; 12,
81. Xia, P; Ma, L; Sun, R; Yang, Y; Tang, X; Yan, D; Lin, T; Zhang, Y; Yi, Y. Evaluation of potential ecological risk, possible sources and controlling factors of heavy metals in surface sediment of Caohai Wetland China. Sci. Total Environ.; 2020; 740, 1:CAS:528:DC%2BB3cXht1OnsL7I 140231.
82. Rani, S; Ahmed, MK; Xiongzhi, X; Keliang, C; Islam, MS; Habibullah-Al-Mamun, M. Occurrence, spatial distribution and ecological risk assessment of trace elements in surface sediments of rivers and coastal areas of the East Coast of Bangladesh. North-East Bay of Bengal Sci Total Environ.; 2021; 801, 1:CAS:528:DC%2BB3MXhvFSrurrJ [DOI: https://dx.doi.org/10.1016/j.scitotenv.2021.149782] 149782.
83. Müller, G. Die Schwermetallbelastung der sedimente des Neckars und seiner nebenflusse eine bestandsaufnahme. Chem. Zeitung; 1981; 105, pp. 157-164.
84. Yadav A, Yadav P. K. Pollution Load Index (PLI) of Field Irrigated with Wastewater of Mawaiya Drain in Naini Suburbs of Allahabad District. Curr World Environ ;13(1). https://doi.org/10.12944/CWE.13.1.15 (2018)
85. Coelho, C; Foret, C; Bazin, C; Leduc, L; Hammada, M; Inacio, M; Bedell, JP. Bioavailability and bioaccumulation of heavy metals of several soils and sediments (from industrialized urban areas) for Eisenia fetida. Sci. Total Environ.; 2018; 635, pp. 1317-1330.2018ScTEn.635.1317C1:CAS:528:DC%2BC1cXot1yntrc%3D
86. Redzovic, Z; Erk, M; Gottstein, S; Peric, MS; Dautovic, J; Fiket, Z; Brkic, AL; Cindric, M. Metal bioaccumulation in stygophilous amphipod Synurella ambulans in the hyporheic zone: The influence of environmental factors. Sci. Total Environ.; 2023; 866, 1:CAS:528:DC%2BB3sXoslGisg%3D%3D 161350.
87. Eriksson, SP; Weeks, JM. Effects of copper and hypoxia on two populations of the benthic amphipod Corophium volutator (Pallas). Aquat. Toxicol.; 1994; 29,
88. Canivet, V; Chambon, P; Gibert, J. Toxicity and bioaccumulation of arsenic and chromium in epigean and hypogean freshwater macroinvertebrates. Arch. Environ. Contam. Toxicol.; 2001; 40,
89. Besser, JM; Brumbaugh, WG; Brunson, EL; Ingersoll, CG. Acute and chronic toxicity of lead in water and diet to the amphipod Hyalella azteca. Environ. Toxicol. Chem.; 2005; 24,
90. Felten, V; Charmantier, G; Mons, R; Geffard, A; Rousselle, P; Coquery, M; Garric, J; Geffard, O. Physiological and behavioural responses of Gammarus pulex (Crustacea: Amphipoda) exposed to cadmium. Aquat. Toxicol.; 2008; 86,
91. Lebrun, JD; Uher, E; Tusseau-Vuillemin, MH; Gourlay-Francé, C. Essential metal contents in indigenous gammarids related to exposure levels at the river basin scale: metal-dependent models of bioaccumulation and geochemical correlations. Sci. Total Environ.; 2014; 466–467, pp. 100-108.2014ScTEn.466.100L1:CAS:528:DC%2BC3sXhslOjsLrN [DOI: https://dx.doi.org/10.1016/j.scitotenv.2013.07.003]
92. Urien, N; Farfarana, A; Uher, E; Fechner, LC; Chaumot, A; Geffard, O; Lebrun, JD. Comparison in waterborne cu, ni and pb bioaccumulation kinetics between different gammarid species and populations: natural variability and influence of metal exposure history. Aquat. Toxicol.; 2017; 193, pp. 245-255.1:CAS:528:DC%2BC2sXhslKqsbvF [DOI: https://dx.doi.org/10.1016/j.aquatox.2017.10.016]
93. Mbandzi-Phorego, N; Puccinelli, E; Pieterse, PP; Ndaba, J; Porri, F. Metal bioaccumulation in marine invertebrates and risk assessment in sediments from South African coastal harbours and natural rocky shores. Environ Pollut.; 2024; 355, 1:CAS:528:DC%2BB2cXhtFyrt7bF [DOI: https://dx.doi.org/10.1016/j.envpol.2024.124230] 124230.
94. Lindqvist, R; Langerholc, T; Ranta, J; Hirvonen, T; Sand, S. A common approach for ranking microbiological and chemical hazards in foods based on risk assessment-useful, but is it possible?. Crit Rev Food Sci Nutr; 2020; 60, pp. 3461-3474.1:CAS:528:DC%2BC1MXit1Gmu7nM
95. Perez-Rodriguez, F. Risk assessment methods for biological and chemical hazards in food; 2020; 1 CRC Press: 527.
96. Tokatlı, C; Varol, M; Ustaoglu, F. Ecological and health risk assessment and quantitative source apportionment of dissolved metals in ponds used for drinking and irrigation purposes. Environ. Sci. Pollut. Res.; 2023; 30,
97. Şimşek, A; Mutlu, E. Assessment of the water quality of Bartın Kışla (Kozcağız) Dam by using geographical information system (GIS) and water quality indices (WQI). Environ Sci Pollut Res Int.; 2023; 30,
98. Şimşek, A; Ustaoğlu, F; Mutlu, E. A comprehensive study of water quality in the Western Black Sea: Implementation of prospective human health risk assessment in Kastamonu Turkey. Environ Monit Assess; 2025; 197, 699. [DOI: https://dx.doi.org/10.1007/s10661-025-14172-6]
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