Role of hydraulic parameters in the concentration and spatial distribution of heavy metals in sediments in a two-stage reservoir

Joanna Jaskuła; Tomasz Dysarz; Joanna Wicher-Dysarz; Mariusz Sojka; Tropikë Agaj; Marcos Sanz-Ramos; Marcin Siepak

doi:10.1038/s41598-026-38103-8

. 2026 Feb 2;16:6958. doi: 10.1038/s41598-026-38103-8

Role of hydraulic parameters in the concentration and spatial distribution of heavy metals in sediments in a two-stage reservoir

Joanna Jaskuła ^1,^✉, Tomasz Dysarz ², Joanna Wicher-Dysarz ², Mariusz Sojka ¹, Tropikë Agaj ³, Marcos Sanz-Ramos ⁴, Marcin Siepak ⁵

PMCID: PMC12916767 PMID: 41629432

Abstract

The role of hydraulic parameters in sediment transport and heavy metals concentration still needs scientific research. In this study, GIS techniques, IBER (a 2D hydrodynamic modeling system) and statistical analysis were applied to assess heavy metals concentration, spatial distribution and sources. A total number of 30 surface sediment samples were collected from the Stare Miasto two-stage reservoir. Results showed that median values follow order Zn > Pb > Cu > Cr > Ni > Cd, which was characteristic for both parts of the reservoir. The overall calculated median concentrations of Zn, Pb, Cu, Cr, Ni, and Cd were 6.74, 1.66, 1.14, 0.99, 0.8, and 0.04 mg/kg. Analysis of heavy metals concentration shows that higher mean values were observed in the pre-dam part for all of the analyzed heavy metals. The highest risk was observed for Zn, Pb and Cd for all of the analyzed samples. Statistical analysis showed that heavy metals concentration is correlated with the fraction of sediments and distance from the inflow. Spearman’s rank correlation showed that hydraulic parameters affect heavy metals concentration. Critical diameter was negatively correlated with Cu while Froude number and velocity were negatively correlated with Cu and Zn concentrations. Also, it was observed that Cu concentrations in the main zone were positively correlated with specific discharge. Results showed that the two-stage construction of the reservoir has an impact on the limitation of sediments spatial distribution and helps to control pollution related to heavy metals.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-38103-8.

Keywords: Heavy metals, Sediments, Two-stage reservoir, IBER, GIS

Subject terms: Ecology, Ecology, Environmental sciences, Hydrology

Introduction

The observed and projected consequences of climate change necessitate political, technical, and environmental decisions that will allow sustainable water management and protection^1–5. One of the most popular solutions is the construction of reservoirs, which have many operational functions, including water management for irrigation purposes, flood control, hydropower, and recreation. The construction of dams causes many negative impacts, including changes in the hydrological regime, sediment transport, and eutrophication⁶. Contamination of hazardous elements, including heavy metals (HMs) in water, sediments, and plants has become one of the most serious threats to surface waters⁷. Accumulation of HMs in aquatic environments can cause oxidative damage to plants, reduce nutrient absorption, and affect animals⁸. Thus, HMs pollution is a challenging environmental problem of growing concern, as it can threaten the health of humans and the ecosystem⁹. The anthropogenic input in the water environment has a significant impact on HM contamination, including agricultural and industrial sources¹⁰. Additionally, bottom sediments can be contaminated by HMs from natural sources, including geological weathering and soil erosion¹¹.

The HMs accumulate in water, bottom sediments, and flora, disturb the aquatic ecosystem, and lead to threats to trophic and human health¹². Most of the hazardous elements are absorbed into fine-grained particles in the bottom sediments. Ecological and health risks are determined by contamination with pollutants; however, their reactivity, mobility, and bioavailability depend on their chemical speciation and sediment properties¹³. Fraction analysis and chemical extraction allow the total contents to be distinguished according to extractability. This process is often used to explore the potential availability and mobility of metals, because easily extractable fractions, which can be distinguished from more strongly bound forms, are less mobile and potentially less hazardous to the environment¹⁴.

Previous research has used a number of geochemical and ecological indices to assess the quality of bottom sediments that might be affected by their metal contents^15–17. Additionally, the calculation of geochemical indices is helpful in assessing sources of pollution, including natural and anthropogenic processes^14,18,19. Advancing knowledge on how catchment properties, reservoir parameters, and hydrological parameters impact HM contamination is considered a priority for water resource management. Additionally, the impact of land cover and anthropogenic sources on HM contamination has been analyzed previously in many studies^20–24. However, it is still necessary to investigate scientifically the impact of hydraulic parameters on sediment deposition and HM concentrations. Previous studies have demonstrated the impact of velocity, but the roles of other hydraulic parameters, including bed shear stress, critical diameter, energy, Froude number, and specific discharge, have not been analyzed before. To address this issue, the present study extends beyond the commonly applied analysis based solely on flow velocity by incorporating a set of hydraulic parameters that better represent the physical mechanisms of sediment transport and heavy metals deposition. Velocity provides a general indication of flow intensity and it does not fully capture the combined effects of flow regime, sediment mobility, and transport capacity that control the accumulation and redistribution of fine particles, which are known to preferentially store heavy metals. The hydraulic parameters including critical diameter, Froude number, and specific discharge show complementary insights into heavy metals, sediments and hydraulic interactions. Integration and full understanding of hydraulic parameters, the study provides a more deep understanding of how normal hydrological conditions impact the spatial distribution and ecological risk of heavy metals in sediments. This approach enables a more accurate identification of contamination-prone zones within a two-stage reservoir system and improves the interpretation of sediment-associated pollution dynamics compared to traditional velocity-based analyses. Hydraulic modeling plays a crucial role in understanding the current situation and in predicting future scenarios. Future predictions of the direct and indirect impacts of climate change, catchment, and morphological and hydraulic parameters on HMs are essential for current risk assessment for surface waters²⁵.

Multi-stage reservoirs, particularly those constructed with a pre-dam, play an important role in sediment management and pollutant retention. The pre-dam acts as a sedimentation basin that reduces flow velocity, thereby promoting the deposition of suspended solids and associated contaminants, such as heavy metals, before they reach the main reservoir. This hydraulic function is essential in controlling the spatial distribution of pollutants. The structural design helps mitigate downstream pollution and maintain water quality by spatially separating zones of contaminant accumulation. Despite their widespread use in water management, the influence of such systems on the spatial distribution and ecological risk of heavy metals in bottom sediments remains underexplored. Therefore, focusing on a two-stage reservoir allows for a better understanding of how reservoir construction and hydraulic parameters affect pollutant trapping efficiency and environmental risk^26–28.

Problems concerning the assessment of HM concentrations are related not only to pollution sources but also to the spatial representation of field measurements. Recently, geographical information systems (GIS) and the Global Positioning System (GPS) have played an important role in monitoring, mapping, and assessing the dynamics of changes in the water environment. Geostatistical tools allow the estimation of parameter values at unknown points and the creation of a continuous dataset for spatial assessment using deterministic and statistical interpolation methods, including kriging, inverse distance weighted (IDW), spline, and trend. Additionally, GIS is used to build, analyze and display spatial data, which can be helpful in decision-making processes in environmental applications.

The main aim of the study was to combine GIS, hydraulic modeling, and statistical tools in a multi-faceted generic risk assessment of HM concentrations (Cd, Cu, Cr, Ni, Pb, Zn) of bottom sediments in the Stare Miasto reservoir, Poland. The study assessed: (1) contamination levels and associated risks according to geochemical indices; (2) the role of hydraulic parameters in heavy metal concentrations and spatial distribution; (3) how sediment transport influence bathymetric changes; (4) the spatial extent and environmental implications of sediment accumulation; (5) the impact of two-stage construction on pollution control.

Materials and methods

Study area

The Stare Miasto is an artificial reservoir located on the Powa River in the central part of Poland (Fig. 1). The total area of the Powa catchment equals 324.27 km², while the total area of the Stare Miasto reservoir is 286.47 km². The Stare Miasto reservoir catchment is mainly covered by agricultural lands (78.83%), forests (18.65%), urban areas (2.03%), and water (0.49%). The reservoir is directly located on arable lands (95%) and urban areas (5%), which are potential sources of heavy metals. Additionally, the A2 highway crosses the main part of the reservoir and significantly impacts flow behavior in the reservoir. Taking into account hydrometeorological conditions, the mean annual rainfall station in the catchment is 540 mm and mean flow in the Powa River is 1.12 m³/s.

The Stare Miasto reservoir was constructed in 2006 and is characterized by two-stage construction, divided into the main and pre-dam zone. Water stored in the main zone, located in the lower part, is used for many functions, including flood protection, irrigation, regulation of the hydrological regime, recreation, and hydropower. The pre-dam zone, located in the upper part, is characterized by a smaller area whose main function is protecting water stored in the main zone. This construction prevent sediment accumulation and degradation of water quality in the main part of the reservoir. The total area of the Stare Miasto reservoir is 90.68 ha, including 27 ha for the pre-dam zone and 63.68 ha for the main reservoir. The total capacity of the reservoir is 2.159 hm³, 0.294 hm³ in the pre-dam zone and 1.865 hm3 in the main part.

Previous research confirmed that surface waters in the Stare Miasto are affected by heavy metals. The results showed that concentration was in order Zn > Cu> Ni > Pb> Cr > Cd. Mean values equal to 0.08 mg/kg for Cd, 0.223 mg/kg for Cr, 1.55 mg/kg for Pb, 2.64 mg/kg for Ni, 6.45 mg/kg for Cu and 16.9 mg/kg for Zn²⁹.

Materials

A total of 30 bottom sediment samples were collected from the Stare Miasto reservoir in October 2018. The measurements were taken in selected transect cross-sections on the basis of reservoir geometry. Finally, 12 sediment samples were taken from the pre-dam zone (four cross sections) while 18 samples were obtained from the main part zone of the reservoir (six cross-sections). The location of the transects was selected based on bathymetry plans of the reservoir, previous research, and preliminary analysis of sediment deposition location using hydraulic software.

The bathymetry for 2013 was measured in cross-sections using GPS and Echotrac CVM depth sounder devices. Bathymetry was calculated based on 98 cross-sections at distances from 50 to 100 m. The 2018 bathymetric data were obtained from measurements made as part of the second phase of the ISOK project (Information System for National Protected against Extraordinary Hazards). It was obtained from 14 cross-sections with distances ranging from 200 to 300 m. Daily hydrological data from 1971 to 2020 from the Posoka water gauge station were downloaded from the website of the Institute of Meteorology and Water Management – National Research Institute (IMGW-PIB).

The boundaries of the Stare Miasto reservoir catchment were determined based on the Hydrographical Map of Poland in scale 1:10,000 developed by the Institute of Meteorology and Water Management. The catchment parameters were characterized based on the Digital Elevation Model (DEM) in ARC/INFO ASCII GRID format with a spatial resolution of 1 m. The land use structure was characterized based on the Topographic Objects Database in vector format on a scale of 1:10,000. The DEM and Topographic Objects Database were provided by the Head Office of Geodesy and Cartography in Poland. Sediment volume information, including measurement points and removal of volume values, were obtained based on an official report from the Geodeta company.

Methods

Field measurements and laboratory analyses

The location of measurement points was recorded using a hand-held Global Positioning System (GPS) device (GARMIN OREGON 300) and is presented in Fig. 1. The samples were collected from the top 0–5 cm depth using Nurek-1 and Czapla devices and carried in zip-sealed PVC packages. In the first step of laboratory work, sediment samples were air-dried at room temperature and then sieved using a nylon sieve to remove coarse debris. Next, the samples were homogenized and passed through 2 mm, 1 mm, 0.5 mm, 0.25 mm, 0.10 mm, and 0.05 mm sieves using an analytical sieve shaker (Model, AS 200). The size fraction of sediments less than 0.05 mm was determined according to the Casagrande aerometric method modified by Prószyński. Additionally, the sand (0.05–2.0 mm), silt (0.002–0.05 mm), and clay (< 0.002 mm) fractions were determined. Next, the sediment samples were extracted using hydrochloric acid (Merck, Darmstadt, Germany) at a 1:4 ratio in a Mars 5 Xpress microwave digestion system (CEM, Matthews, NC, USA). The concentrations of Cd, Cr, Cu, Ni, Pb, and Zn in fractions of < 2 mm were determined by inductively coupled plasma mass spectrometry (ICP-QQQ-MS), model 8800 Triple Quad (Agilent Technologies, Japan). All reagents used were analytically pure; water was purified to the resistivity of 18.2 MΩ.cm at 25 °C using a Direct-Q UV3 Ultrapure Water Systems device (Millipore, France). Analysis using ICP-QQQ-MS techniques was applied based on standard solutions produced by VHG Labs, Inc. (Manchester, England). Additionally, certified reference material (CRM) no. LGC 6187, which was used for sediments (Manchester, England), was applied with high compliance with reference values.

Geochemical indices

The Geoaccumulation Index (Igeo) is one of the most widely used methods to interpret the sediment contamination for each sampling station, which takes into account the natural fluctuations of the HMs in the environment³⁰. Igeo was first proposed by Müller³¹, representing the pollution intensity in sediments, and is calculated using Eq. 1.

where C_i is the concentration of HM in the sediments (mg/kg) and B_i is the reference geochemical background value of each HM. Background values used in this study were adopted for the area of Poland, and were: Cd 0.5 mg/kg, Cu 6 mg/kg, Cr 5 mg/kg, Fe 10,000 mg/kg, Ni 5 mg/kg, Pb 10 mg/kg and Zn 48 mg/kg³². According to the value of Igeo, sediments were classified into seven classes: Igeo ≤ 0 (no enrichment), 0 < Igeo ≤ 1 (minor enrichment), 1 < Igeo ≤ 2 (moderate enrichment), 2 < Igeo ≤ 3 (moderately severe enrichment), 3 < Igeo ≤ 4 (severe enrichment), 4 < Igeo ≤ 5 (very severe enrichment), and 5 < Igeo (extremely severe enrichment).

The Pollution Load Index (PLI) allows to identify the contamination in relation to several trace metals and compare samples taken from different locations³³. Additionally, the PLI provides a holistic view of the HM contamination in the analyzed area. It was proposed by Tommilson et al.³⁴ and is calculated using Eq. 2.

where n is the number of HMs and CF is the contamination factor calculated for each HM using Eq. 3.

Based on the PLI values, the sediments can be classified into PLI < 1 (no pollution) and PLI ≥ 1 (pollution) classes.

The Metal Pollution Index (MPI) represents overall HM contamination in bottom sediments as a single value which allows one to compare the total content of all HMs in sediments at different sample collection stations³⁵. The MPI was calculated using Eq. 4.

Ecological risk assessment

The Toxic Risk Index (TRI) was used to determine the integrated toxic effects of groups of HMs³⁶. The TRI was used to assess the toxic effects of HMs in sediments including rivers, lakes, and reservoirs³⁷. It was proposed by Zhang et al.³⁸ and is calculated using Eq. 5.

where TRI_i is the value of TRI calculated for a single heavy metal and was calculated using Eq. 6.

where TEL is the threshold effect level, and PEL is the probable effect level³⁹. The TRI values were assigned to five categories: TRI ≤ 5 (no toxic risk), 5 < TRI ≤ 10 (low toxic risk), 10 < TRI ≤ 15 (moderate toxic risk), 15 < TRI ≤ 20 (considerable toxic risk), and TRI > 20 (very high toxic risk).

The Potential Ecological Risk Index (PERI) was used for a comprehensive assessment of concentration, toxic level, and the ecological effect of HMs⁴⁰. It was also applied to assess the toxic level of sediments in aquatic environments, including marine and inland waters⁴¹. The PERI can be calculated using Eq. 7.

where ER_i is the value of the PERI calculated for a single heavy metal, calculated using Eq. 8:

where TRi is the toxic response factor and equals 30 for Cd, 5 for Cu, 2 for Cr, 6 for Ni, 5 for Pb and 1 for Zn⁴², while CF is the contamination factor for each HM calculated based on Eq. 8. The PERI values were assigned to five categories: PERI < 150 (low), 150 ≤ PERI < 300 (moderate), 300 ≤ PERI < 600 (high), and PERI ≥ 600 (very high).

Hydraulic modeling

Hydraulic modeling was performed using IBER software—free, open-source software (www.iberaula.com) distributed by Grupo de Enxeñaría da Auga e do Medio Ambiente GAEMA and Flumen Institute from Spain. The hydrodynamic computations were based on 2D shallow water equations solved with the finite volume method. Detailed descriptions of the solver were provided by Bladé et al.⁴³ and Sanz-Ramos et al.⁴⁴. The solver is implemented in the GID user interface (www.gidsimulation.com). GID is software aimed at modeling, simulation, and visualization for mechanical applications. It enables the usage of GIS and CAD formats to prepare the spatially distributed model. In general, IBER works like a specific plug-in which enables the usage of the options available in the former.

The model applied in this research was previously used and tested in the study on potential failure of the internal dam^44,45. To model, two boundary conditions were imposed: inlet and outlet. In the inlet, the imposed flow hydrograph was constant and equals the value of the mean flow 1.12 m³/s determined based on observations in the Posoka gauges station. In the outlet located on the main dam, the constant water surface was implemented with the value of the normal level equal to 93.5 m a.s.l. The mean flow was selected in this study to assess the influence of hydraulic parameters on the distribution of heavy metals under normal hydrological conditions, which occur the most frequently in the reservoir. This approach allows for an assessment of heavy metal spatial distribution and the identification of zones affected by contamination during planned reservoir operation. Additionally, an unstructured mesh of quadrilaterals was applied with a greater density of elements close to the sensitive points like an internal dam, the bridge, and the main dam. The greater mesh density is also implemented in the zone close to the inlet. The bottom of the reservoir was composed of soils typical of the Powa River while near main dam, the concrete was implemented. The internal dam is made of steel walls and stones which were also used to strengthen the bottom under the bridge. The IBER model was used in its standard configuration without any modifications to the source code or numerical solvers. The purpose of its application in this study was to extract key hydraulic parameters including bed shear stress (BS), critical diameter (CDi), energy (EN), Froude number (FN), velocity (VE), and specific discharge (SD) in order to evaluate their spatial relationships with sediment deposition and heavy metal distribution. Validation of hydraulic modelling results using this model has been presented in previous studies^45,46.

Sediments volume

To calculate bathymetry changes between 2013 and 2018 and sediment volume, five spatial interpolation methods in GIS were tested including IDW, Natural Neighbor, Linear, Ordinary Kriging, and Spline. The applied methods including algorithms and tools were available in the ArcGIS Pro software. The leave-one-out cross-validation procedure was applied to estimate the performance of interpolation methods to train and validate the input parameters. To verify the obtained results, the depth raster from 2018 was compared to the interpolated depth raster obtained based on field measurements. Additionally, to determine the choice of interpolation methods, root mean square error (RMSE) was calculated using Eq. 9.

where zʹ(x_i) is the predicted value based on the interpolation method while z(x_i) is the observed value.

Finally, the spatial interpolation of field measurement points was conducted based on the IDW method (Table 1). The basic principle of IDW interpolation is using a weighted linear combination set of sample points. It relies on two statistical and mathematical methods to create surfaces and calculate the predictions of unmeasured points. The general equation used for the IDW was presented in Eq. 10.

Table 1.

Results of interpolation methods.

Method	RMSE	R ²	p
IDW	0.235	0.815	< 0.05
Natural Neighbor	0.512	0.613	0.115
Linear	0.709	0.217	0.517
Ordinary Kriging	0.365	0.633	< 0.05
Spline	0.675	0.210	0.550

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where z is the interpolated value, x_i is the neighboring data points, and d_ij is the distance between the interpolated and data points.

Additionally, a barrier option was applied as a breakline, which limited the search and application for input sample points. The pre-dam was represented as a barrier in the form of a polyline, which was a barrier and interruption of sediment transport. The barrier option allows the application of only input measurement points located on the same side of the barrier as the processing cell was considered. In the applied option, the breakline, which represents the pre-dam, entirely stops the search across the pre-dam, allowing analysis of the actual operating conditions of the reservoir.

Statistical methods

The distributions of Cd, Cr, Cu, Ni, Pb and Zn, were characterized based on the one-sample Shapiro-Wilk test. Analyzed HMs considered differed from the normal distribution, and nonparametric methods were applied. The Mann-Whitney U test was used to assess differences in the median values of heavy metal concentrations in sediment samples collected from the pre-dam and main zone. The correlations between the HM concentrations were evaluated using Spearman’s rank correlation coefficient. Cluster analysis (CA) was applied to determine the similarities and differences between sampling sites based on the HM concentrations. The CA was carried out using the Ward method with square Euclidean distance as a measure of similarity between clusters. To assess cluster stability, cophenetic correlation was calculated. Additionally, the Mann-Whitney U test was used to evaluate differences between concentrations of HMs in distinguished groups. Then, a Spearman’s rank correlation analysis was used to expose variables associated with heavy metal concentrations in bottom sediments. The following variables were distinguished during the study: (1) sampling point location characterized by distance to river (DR), distance to reservoir border (DB), distance to inflow (DI), distance to outflow (DO) and water depth (D); (2) sediment texture characterized by the content of sand (SA), silt (SI) and clay (CL) fractions, and (3) hydraulic conditions outlined by bed shear stress (BS), critical diameter (CD), energy (EN), Froude number (FN), velocity (VE), and specific discharge (SD). Statistical analyses were carried out at a significance level of 0.05 using the Canoco 5.0, STATISTICA 13.1, and R 3.0 software.

Results

HM concentrations

The HMs’ characteristic concentrations in sediment samples are shown in Table 2. Median values in the Stare Miasto reservoir follow the descending order Zn > Pb > Cu > Cr > Ni > Cd, which was characteristic for both parts of the reservoir. The overall calculated median concentrations of Zn, Pb, Cu, Cr, Ni, and Cd were 6.74, 1.66, 1.14, 0.99, 0.8, and 0.04 mg/kg, respectively. Overall values showed that Zn varied from 2.62 to 44.93 mg/kg with a mean value of 10.02 mg/kg. Despite the prior removal of sediments from the pre-dam zone, the concentrations of heavy metals particularly Zn, Pb, and Cu—remained higher in this area compared to the main part of the reservoir. This persistent contamination may result from continuous pollutant input from the Powa River, especially from the arable lands which cover most of the analyzed catchment. Additionally, lower flow velocities in the pre-dam zone likely enhance sedimentation, allowing fine particles such as silt and clay to settle and bind with heavy metals, which explains the sustained high HM concentrations in this area despite dredging efforts. Moreover, the dredging activity may have disrupted the sediment structure, potentially exposing deeper contaminated layers or facilitating the resuspension of bound metals. In addition, adsorption of heavy metals onto newly deposited fine sediments could contribute to sustained accumulation. These processes suggest that while dredging reduces the sediment volume, it may not fully mitigate long-term pollution risks in hydrodynamically active zones such as the pre-dam area.

Table 2.

Characteristic concentrations of HMs in mg/kg in the stare miasto reservoir.

HM	Cd	Cu	Cr	Ni	Pb	Zn
Overall (N = 30)
Minimum	0.02	0.62	0.40	0.26	0.66	2.62
Maximum	0.35	7.54	6.65	5.69	11.98	44.93
Mean	0.07	1.79	1.56	1.26	3.24	10.02
Median	0.04	1.14	0.99	0.80	1.66	6.74
SD	0.08	1.65	1.34	1.12	3.24	9.63
Pre-dam zone (N = 12)
HMs	Cd	Cu	Cr	Ni	Pb	Zn
Minimum	0.02	0.64	0.40	0.32	0.66	3.37
Maximum	0.22	5.14	3.86	2.70	11.12	26.45
Mean	0.09	2.09	1.59	1.21	4.05	11.30
Median	0.06	1.52	1.05	0.97	2.56	7.12
SD	0.08	1.56	1.25	0.88	3.63	8.79
Main part (N = 18)
HM	Cd	Cu	Cr	Ni	Pb	Zn
Minimum	0.02	0.62	0.59	0.26	0.94	2.62
Maximum	0.35	7.54	6.65	5.69	11.98	44.93
Mean	0.06	1.59	1.53	1.29	2.70	9.17
Median	0.03	0.99	0.99	0.78	1.45	5.33
SD	0.08	1.72	1.44	1.29	2.93	10.32

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In the main part, the highest values were observed near the highway-influenced inflow zone, which transports fine, metal-enriched particulates. High HM concentrations may be related to anthropogenic pollution from road infrastructure. The highest concentrations of Cd, Cu, Cr, Ni, and Zn, were found in the main part of the reservoir, while that of Pb was observed in the pre-dam zone. The lowest concentrations for the analyzed HMs were at similar levels for both parts of the Stare Miasto reservoir. Taking into account median values in parts of the reservoir, higher median values were observed in the pre-dam part for all of the analyzed HMs. The sediments in the pre-dam zone were removed in 2013 to increase the volume of this part. The analysis showed that after six years, sediments stored in the pre-dam zone showed similar or higher mean values compared to the main part. The Mann-Whitney U test showed (at a significance level of 0.05) no significant differences between the concentrations of heavy metals in the sediments between the pre-dam and main zones. It confirms that the pre-dam stops the inflow of sediments to the main part and stores pollutants in the pre-dam zone. The pre-dam part is constructed to accumulate sediments and pollutants, which according to the reservoir management plan, sediments deposited in the pre-dam should be removed regularly. During the analyzed period, sediment removal from the pre-dam zone was carried out in 2013, whereas no sediment removal occurred in the main reservoir since 2005. Given that the concentrations of heavy metals in sediments from both sections do not differ significantly, this indicates that the pre-dam zone accumulated contamination to a level comparable with the main reservoir within a substantially shorter time. Specifically, sediments in the pre-dam represent an accumulation period of approximately 5 years from 2013 to 2018, while those in the main reservoir reflect about 12 years of deposition from 2005 to 2018. This implies that pollutant accumulation in the pre-dam occurs approximately 2.5 times faster than in the main reservoir, demonstrating the effectiveness of the pre-dam in capturing and retaining contaminants.

The correlation analysis showed that all HMs in the Stare Miasto reservoir sediments were significantly correlated with each other, indicating their common origin (Fig. 2). The highest correlations were observed between Cd and Cu, and between Cu and Zn, signifying their strong geochemical association. Similar results were obtained for reservoir parts, including the pre-dam zone and main part. Slightly lower correlations of HMs were observed in the main part of the reservoir, especially between Cd and Cd, and between Cd and Ni.

Fig. 2 — Correlation analysis for individual HMs in sediment samples a) overall b) pre-dam zone c) main reservoir

Geochemical indices

The Igeo values show that bottom sediments from the Stare Miasto reservoir were not polluted; all of the analyzed samples showed values below 0 (Fig. 3). The highest values occurred in sample 17, which is located in the middle part of the main reservoir near the highway. Taking into account each heavy metal concentration, the lowest values of Igeo for most of analyzed locations occurred for Cd, and the highest for Cu, Cr and Pb, in both parts of the reservoir.

Fig. 3 — I_geo values for the Stare Miasto reservoir (software: StatSoft, Statistica 14.1.0.4.).

The values of the PLI in the Stare Miasto reservoir ranged between 0.072 and 1.071, with a mean value of 0.24 (Appendix A). The study indicated that only the PLI value for sample site no. 17 located in the main reservoir was greater than 1, indicating pollution. The calculated PLI values for other sample locations were less than 1, indicating no pollution. The comparison between the PLI mean values showed a slightly higher value in the pre-dam (0.27), while in the main reservoir it was 0.22. Similar results were obtained for the MPI. The highest value was observed for sample 17, located in the main part, but mean values showed that the pre-dam zone was characterized by higher values compared to the main reservoir (Appendix A).

In contrast, the TRI and PERI values show that ecological risk was observed in more sediment samples. According to the TRI, moderate ecological risk occurred in sample 17, with a value 10.09. Additionally, low risk was observed for samples 4, 6, and 11 in the pre-dam zone and 26 in the main reservoir; other samples showed no ecological risk (Appendix A). The mean value of the TRI was 3.30 in the pre-dam zone and 2.25 in the main part of the reservoir. According to the percentage of each heavy metal in total TRI values, Pb and Zn represented the highest risk in all of the analyzed samples. Similar to the TRI, the highest PERI value (43.95) was observed in sample 17. The PERI showed that the highest risk was related to Cd for all analyzed samples. The differences between results from the TRI and PERI can be related to the different representation of ecological risk. The TRI primarily focuses on the toxicological risk posed by combined heavy metals effects, emphasizing their relative toxicity and concentration compared to reference using TEC and PEC values, which are defined as general for sediments in surface waters. The PERI includes background values of heavy metals, which are related to the geographical area, and provides a more comprehensive ecological perspective for evaluating long-term ecological impacts of heavy metals in sediments.

Hydraulic modeling

The Stare Miasto reservoir is located on the Powa River, where sediment transport is modified by hydrological conditions. Flow velocity in morphodynamical processes, including sediment deposition, plays an important role. Spatial changes of velocity were observed in both parts of the Stare Miasto reservoir. Areas with low velocity reduce the capacity for sediment transport, which facilitates the accumulation of fine-grained materials that typically carry adsorbed heavy metals. In contrast, higher-velocity zones may limit deposition and encourage remobilization of previously settled metals. The highest values (0.71 m/s) were observed near the inlet, pre-dam, and main dam (Fig. 4). Higher velocity was also observed near the highway located in the middle part, which changed the shape of the reservoir. Additionally, it was observed that the old Powa riverbed plays an important role in velocity values, which are higher in this location.

Fig. 4 — Spatial distribution of hydraulic parameters in the Stare Miasto reservoir in 2018 on the basis of IBER modeling (software: ESRI, ArcGIS Desktop 10.8.1.).

Results showed that BS, CDi, EN, and water level were at a similar level along almost all reservoir parts. The maximum BS value of 5.17 N/m² was observed only at the pre-dam, which was related to the flowing water between the pre-dam and main parts. High values of BS in this zone suggest strong hydraulic forces that may trigger localized erosion, resuspending sediment-bound metals and redistributing them further downstream or toward the central reservoir. EN values varied from 93.50 to 93.58 with spatial distribution different in three parts of the reservoir. The highest values were observed near the inlet and outlet of the pre-dam part, at 93.58, while the lowest values were obtained for the main part of the reservoir. FN shows a high spatial distribution, especially in the pre-dam zone (Fig. 4). The highest values (0.63) were observed near the inlet, pre-dam, highway, and outlet of the reservoir. High values were also observed in the middle part of the pre-dam zone. Similar to the FN, SD shows a high spatial distribution. The highest values (0.16 m²/s) were observed near the inlet, pre-dam, and outlet of the reservoir. Additionally, the highest values were observed along the old riverbed of the Powa River. Values of selected hydraulic parameters in the analyzed locations were presented in the Appendix B.

The obtained results showed that for all of the analyzed parameters, the mean value is higher in the pre-dam zone of the Stare Miasto reservoir. These hydrodynamic patterns help explain the observed spatial variation in heavy metal concentrations. The pre-dam zone, where VE, FN, and SD are lower in central areas, provides favorable conditions for the deposition of fine particles that tend to carry heavy metals such as Zn, Pb, and Cd. In contrast, higher values of SD and EN near the inflow and outlet zones may promote resuspension and short-distance redistribution of pollutants. The multi-stage structure of the reservoir, particularly the separation of the pre-dam and main reservoir zones, appears to regulate both hydraulic energy and pollutant retention. These interactions highlight the importance of reservoir construction in managing contaminant deposition dynamics.

Sediment volume

Analysis of bathymetry between 2018 and 2013 showed that the accumulation and erosion of sediments are visible in both parts of the reservoir (Fig. 5). The erosion process is visible only near the banks in the main part of the reservoir. The accumulation process is visible in the total area of 48.35 ha, which is 75.93% of the main part. Erosion in the pre-dam zone was observed only near the pre-dam, while accumulation was visible in all areas (Fig. 5c). Additionally, the Stare Miasto reservoir was reconstructed, including the location of the pre-dam and removal of 40 cm of sediments from the pre-dam zone in 2013. The removed sediments, with an estimated volume of 8,000 m³, were distributed along the reservoir banks (Fig. 5d). The results showed that during 6 years from 2013 to 2018, the total area of accumulated sediments was 22.63 ha, which is 83.81% of the pre-dam zone with a volume equals 8,761 m³. It confirms that sediment transport affects the analyzed reservoir and the accumulation of sediments leads to an increase in HMs concentration. Accumulation of the sediments in some areas changes the depth of the reservoir and affects hydraulic conditions, lower values of velocity and other parameters lead to an increase in selected heavy metals. Sample 17 characterized by the highest ecological risk and HMs concentration is located in the area of the reservoir characterized by the highest accumulation of sediments.

Fig. 5 — Changes in depth between 2013 and 2018 in the Stare Miasto reservoir (software: ESRI, ArcGIS Desktop 10.8.1).

Statistical analyses

Cluster analysis (CA) was applied to categorize sediment samples according to the concentration of HMs. The dendrogram of the CA is presented in Fig. 6a, while the spatial interpretation of statistics is shown in Fig. 6b. The results showed that sediment samples can be divided into two main groups where samples have similar concentrations of HMs. Most of the analyzed samples were assigned to Group 1, while only sample sites 4, 6, 11, 17, and 26 were in Group 2. Group 2 samples were characterized by the highest ecological risk. Samples in Group 1 were mainly from the main part of the Stare Miasto and were characterized as uncontaminated. Group 2 samples were mainly from the pre-dam zone, where sediments consist of HMs from the inflowing Powa River. Additionally, in Group 2 there was included sample 17 from the main part, where a high concentration of HMs was related to inflowing pollution from the highway.

Fig. 6 — Cluster analysis for sediment samples in the Stare Miasto reservoir (software: ESRI, ArcGIS Desktop 10.8.1.).

Based on the results of the Mann-Whitney U test, the concentrations of heavy metals in the bottom sediments of sediment sites included in Group 2 were higher than those of samples included in Group 1. In the samples included in Group 2, there was a lower proportion of the sand fraction and a higher proportion of the silt and clay fraction. The cophenetic correlation, calculated as a statistical measure of the dendrogram, showed a mean value of 0.781, which confirms relatively high accuracy for representing the relationships in the original data.

Spearman’s rank correlation coefficient was applied to assess environmental variables that can affect HM concentrations in sediments. The results of this analysis for the whole reservoir showed that the contents of the silt and clay fractions were positively correlated with the concentrations of individual heavy metals in the sediments, and the contents of the sand fraction were negatively correlated (Table 3). The results showed that sediment texture is more strongly related to heavy metal concentrations in the pre-dam zone than in the main zone. This is especially relevant to the content of SA and SI fractions. However, regarding the content of CA fractions, higher correlations with Cd, Cu, and Zn were obtained in the pre-dam zone, but for Cr, Ni, and Pb in the main zone. The results of Spearman’s rank correlation coefficient analysis for the pre-dam reservoir showed that CD was negatively correlated with Cu concentrations while FN and VE were negatively correlated with Cu and Zn concentrations. Pb concentrations in the pre-dam zone were positively correlated with DI. In contrast, Cu concentrations in the main zone were positively correlated with SD.

Table 3.

Spearman’s rank correlation results of selected parameters; red values indicate statistically significant correlations at p < 0.05.

Zone	Parameter	Cd	Cu	Cr	Ni	Pb	Zn
Overall (N = 30)	SA	−0.73	−0.76	−0.71	−0.72	−0.82	−0.75
	SI	0.70	0.71	0.64	0.66	0.77	0.68
	CL	0.63	0.74	0.79	0.67	0.67	0.76
	CD	−0.06	−0.07	−0.06	−0.07	−0.13	−0.03
	FN	−0.09	−0.09	−0.06	−0.08	−0.13	−0.04
	VE	−0.05	−0.07	−0.07	−0.06	−0.12	−0.04
	SD	0.03	0.01	0.01	0.08	−0.05	0.03
	DI	−0.24	−0.14	0.14	0.15	−0.13	−0.12
Pre-dam part (N = 12)	SA	−0.95	−0.89	−0.96	−0.93	−0.89	−0.93
	SI	0.92	0.85	0.95	0.92	0.94	0.87
	CL	0.77	0.81	0.67	0.61	0.46	0.84
	CD	−0.46	−0.63	−0.45	−0.55	−0.38	−0.56
	FN	−0.51	−0.69	−0.48	−0.55	−0.41	−0.62
	VE	−0.50	−0.66	−0.49	−0.57	−0.42	−0.61
	SD	−0.42	−0.57	−0.40	−0.50	−0.36	−0.51
	DI	0.26	0.28	0.47	0.44	0.59	0.17
Main part (N = 18)	SA	−0.45	−0.52	−0.58	−0.70	−0.69	−0.51
	SI	0.39	0.43	0.46	0.61	0.58	0.40
	CL	0.49	0.67	0.82	0.74	0.75	0.72
	CD	0.05	0.28	0.19	0.23	0.08	0.19
	FN	0.01	0.26	0.22	0.23	0.06	0.19
	VE	0.10	0.30	0.19	0.25	0.14	0.20
	SD	0.27	0.48	0.34	0.47	0.32	0.35
	DI	−0.06	0.06	0.26	0.17	−0.02	0.19

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Discussion

The results obtained in this study confirm that HM concentrations and their spatial distribution are influenced by hydraulic parameters, as well as morphological and sediment characteristics. HM concentrations have been analyzed according to their ecological risk in many previous studies, including the impact on catchment and morphological parameters of the reservoir in their spatial distribution^22,35,47. In this study, the impact of urban sources was observed especially in sample location 17 near highway which was characterized by highest heavy metal concentrations. It was confirmed in previous studies that road infrastructure has the potential to increase sediment heavy-metal ecological risks⁴⁸. Results obtained by Nawrot et al.⁴⁹ showed that concentration of Cd, Cr, Ni and Zn was related with vehicle traffic, confirming road infrastructure-related heavy metal inputs, while Gao et al.⁵⁰ noted that the high ecological risk was associated also with Pb. The concentration and calculated TRI and PERI values in this study confirm previous results showing that Zn, Pb, and Cd represent the high ecological risk.

The impact of catchment parameters and impact of land use was analyzed in many studies but role of hydraulic parameters in heavy metals spatial distribution still needs scientific attention. To full understand this role, this study show the impact of hydraulic parameters based on IBER modeling, being the first study focused on this scientific area. Wicher-Dysarz⁴⁶ reported that processes of accumulation and erosion were observed in locations of significant velocity changes in the Stare Miasto reservoir. The correlations observed in this study can be explained by known hydrodynamic mechanisms. For instance, higher flow velocity and Froude number tend to increase the transport capacity of the river, reducing sedimentation and thereby lowering heavy metal accumulation in those zones. In contrast, low-velocity areas favor sediment deposition, especially fine-grained particles such as silt and clay, which are known to store heavy metals. This dynamic explains the elevated concentrations of Zn and Cu in slower-flowing regions, as well as the role of specific discharge in controlling localized enrichment. These findings support the hypothesis that hydraulic parameters play a key role in the spatial partitioning of heavy metal contamination in reservoirs. A similar pattern was observed in the Three Gorges Reservoir, where sediment grain size and flow regulation played dominant roles in shaping the vertical and spatial distribution of HMs⁵¹. Their study confirmed that fine sediment particles, resulting from reduced flow velocity due to dam operation, significantly enhanced the deposition of metals such as Cd and Pb. This supports finding in this study that flow deceleration in the pre-dam zone facilitates metal accumulation, particularly in zones with higher shear stress and lower sediment flushing capacity. The influence of bed shear stress on sediment mobilization has also been highlighted by Lv et al.⁵², who conducted hydrodynamic simulations of a landslide-dammed lake outburst in the Jinsha River. Their results revealed that spatial variability in shear stress significantly dictated erosion and deposition zones along the channel, with higher stresses corresponding to upstream erosion and downstream sediment accumulation. Notably, critical shear thresholds were exceeded for coarse particles (up to 5 cm in size), confirming the hydraulic capacity to mobilize substantial sediment loads under extreme flow events. While their context differs, these findings reinforce the significance of bed shear stress as a controlling parameter in shaping sediment dynamics an insight that supports our interpretation of heavy metal deposition patterns in the Stare Miasto reservoir. Similar to results obtained in this study, Zhu et al.⁵¹ emphasized that hydraulic regulation, especially anti-seasonal impoundment, directly affects the stratification of sediments and associated metal content, reinforcing the idea that hydrodynamics are crucial in long-term contamination patterns. Additionally, the correlation values depend on the number of sediment samples. The relatively weak correlation may be due to the limited number of samples.

Previous studies confirm the relation between velocity and HM concentration, which play a critical role in controlling the transport, dispersion, and accumulation of heavy metals in aquatic systems. Changes of velocity influence sediment dynamics, which in turn govern the adsorption and desorption processes of metals, including Cd, Cu, Cr, Pb and Ni. High values of flow velocities enhance metal mobilization through sediment resuspension, leading to increased dissolved and particulate metal concentrations in the water column, whereas low-flow conditions favor sedimentation and localized metal accumulation. In this study, it was confirmed that areas with low velocity reduced the capacity for sediment transport and facilitates the accumulation of fine-grained materials that typically carry adsorbed heavy metals. In contrast, higher-velocity zones limit deposition and encourage remobilization of previously settled metals. Also, additional parameters, including BS, CD, EN, FN and SD, were analyzed. The results based on Spearman’s rank correlation show that hydraulic parameters affect heavy metal concentrations. The CD was negatively correlated with Cu, while FN and VE were negatively correlated with Cu and Zn concentrations. Also, it was observed that Cu concentrations in the main zone were positively correlated with SD. Correlation and the PCA analysis in this study showed that HMs show a similar distribution. Additionally, the results indicated that all the analyzed samples were characterized by high pollution of similar HMs, including Zn, Pb, and Cd. This corroborates other reported results based on Pearson correlation, which showed that Cd, Cr, Cu, Ni, Pb, and Zn in sediments were related with anthropogenic sources^53–55. The source was related to industrial and wastewater, vehicle traffic, and runoff from urban areas. The results confirm previous analysis for the Stare Miasto reservoir obtained by Siepak and Sojka²⁹, showing that the concentration of HMs was related to agricultural activity in the catchment area and point sources of contamination. Cabral et al.⁵⁶ reported that sediment textures (clay and silt) and higher organic matter content significantly influenced the accumulation of Cu, Cd, and Zn in Brazilian hydropower reservoirs. Similar results were presented by Yang et al.⁵⁷, where grain size was a dominant factor controlling heavy metal distribution. Results obtained in this study confirm a correlation with grain size and allow to more deep understanding of parameters that have an impact on heavy metals deposition. It is the first study that confirms a direct relationship with heavy metals concentration including Cd, Cu, Cr, Ni, Pb, Zn and hydraulic parameters. Additionally, the obtained results showed that sediment texture has a different relationship with hydraulic parameters comparing to heavy metals concentration. This study confirms that the concentration and spatial distribution of heavy metals are related to sediment texture but also modified by hydraulic parameters.

Obtained results confirmed the role of pre-dam zone in limit transport of pollution. The construction of a two-stage reservoir is a promising solution to store sediments and pollution in the pre-dam zone and protect the main part^6,29,45. Previous results obtained by Siepak and Sojka²⁹ confirm that the Stare Miasto reservoir is characterized by higher values of HMs in the pre-dam zone. Additionally, similar relationships were described by Cabral et al.⁵⁶, who found that low-velocity zones were pollution hotspots, supporting our interpretation that hydraulic and geochemical conditions jointly control spatial heavy metal patterns in sediments. Also, it simplifies removing sediment layers without limiting the operation function of the reservoir. The obtained results confirm previous studies, demonstrating that the Stare Miasto reservoir limits inflow of pollutants to the main part. Even sediments were removed from the pre-dam zone, and the concentrations of HMs were at the same limit or higher compared to the main part. Additionally, the results from this study were compared to HM assessment in the pre-dam zone of the Stare Miasto reservoir from 2011²⁹. The results from this study confirm previous analysis and show that in the pre-dam zone the highest values were observed for Zn, Pb, and Cu. It confirms that the results obtained in 2018 are characteristic of longer-term conditions. Hydraulic parameters play an important role in reservoir protection; depending on the flow and hydraulic conditions in the area of the reservoir, the scale of the analyzed problem can vary. However, even the pre-dam zone limits inflow and protects the main part from pollutants transported by the river, it is not possible to stop this process. Pollutants are transported by flowing water to sediments located in the main reservoir. Also, contamination from areas near the main reservoir can flow into this part directly. The analysis shows the volume of accumulated sediments increased in both parts of the reservoirs. High accumulation of sediments in the location of sample 17 characterized by the highest HM concentrations confirm that pollution can be transported by the river to other locations where this process is not possible to control.

Conclusion

The study aimed to assess the impact of hydraulic parameters on the concentration and spatial distribution of HMs in the Stare Miasto reservoir, which is characterized by two-stage construction. Sediment transport and accumulation processes had important impacts on the spatial distribution of HMs in sediments. The role of hydraulic parameters in the concentrations of HMs still requires further scientific investigation.

The obtained results showed that:

The correlation analysis showed that all HMs reservoir sediments were significantly correlated with each other in both parts of the reservoir, while the highest risk was observed for Zn, Pb, and Cd for all the analyzed samples. Cd, Pb and Zn accumulated in sediments can significantly affect water resources with long-term sources of contamination. Different hydraulic and environmental conditions including flow velocity and sediment disturbance can lead to remobilization of HM from sediments into the water, deteriorating water quality. High concentration of Cd, Pb and Zn can be toxic to aquatic organisms lead to serious risks to aquatic ecosystems based on bioaccumulation. High concentration of HM in sediments represents a critical concern for water resource management and environmental protection in the reservoir.
Spearman’s rank correlation confirmed that hydraulic parameters have an important impact on HM concentrations, including Froude number (FN), velocity (VE), and specific discharge (SD). It confirmed that the impact of hydraulic parameters on HM concentrations is a key factor in assessing contamination levels, ecological risk, and the effectiveness of water resource management strategies in the Stare Miasto reservoir.
The two-stage construction of the Stare Miasto reservoir has an impact on the limitation of the spatial distribution of sediments and the concentration of HM. Results showed that even though sediments from the pre-dam zone were removed, the concentrations of HMs in the sediments were at a similar or higher level compared to the main reservoir, where such works have never been carried out. It confirms that the two-stage construction reservoir plays an important role in pollution control by limit inflow of sediment and contamination to the main part. The pre-dam zone is constructed to reduce flow velocity and plays an important role in the deposition of HM in sediments. It allows to limit the transport of pollutants into the main part of the reservoir. Two-stage construction is effective for controlling pollution in sediments and improving water management in the analyzed reservoir.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(120.1KB, docx)}

Supplementary Material 2^{(15.1KB, docx)}

Author contributions

Conceptualization J.J; Methodology J.J., T.D. and M.S.; Software J.J, T.D. and M.S.R; Validation J.J, T.D. and M.S.R; Formal analysis J.J and M.S.; Resources J.J, J.W.D., M.S. and M.Si.; Data Curation J.J, T.D., J.W.D. and M.S.; Writing - Original Draft J.J, T.D., J.W.D., M.S. and T.A.; Writing - Review & Editing J.J and T.A.; Visualization J.J; Supervision J.J; Project administration J.J. All authors reviewed the manuscript.

Data availability

Data will be available upon request, please contact with first author of the paper.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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References

1.Biswas, R. R., Sharma, R. & Gyasi-Agyei, Y. Adaptation to climate change: A study on regional urban water management and planning practice. J. Clean. Prod.355, 131643. 10.1016/j.jclepro.2022.131643 (2022). [Google Scholar]
2.Bytyqi, V. & Agaj, T. Spatio-temporal distribution of renewable freshwater resources and their availability in Kosovo an analysis from the Eastern region. J. Bulg. Geogr. Soc.50, 35–52. 10.3897/jbgs.e115814 (2024). [Google Scholar]
3.Dąbrowska, J. et al. Between flood and drought: how cities are facing water surplus and scarcity. J. Environ. Manage.345, 118557. 10.1016/j.jenvman.2023.118557 (2023). [DOI] [PubMed] [Google Scholar]
4.Haro-Monteagudo, D., Palazón, L., Zoumides, C. & Beguería, S. Optimal implementation of climate change adaptation measures to ensure long-term sustainability on large irrigation systems. Water Resour. Manage.37 (8), 2909–2924. 10.1007/s11269-022-03225-x (2023). [Google Scholar]
5.Pulley, S. & Collins, A. L. Using the colour of recent overbank sediment deposits in two large catchments to determine sediment sources for targeting mitigation of catchment-specific management issues. J. Environ. Manage.336, 117657. 10.1016/j.jenvman.2023.117657 (2023). [DOI] [PubMed] [Google Scholar]
6.Jaskuła, J., Sojka, M. & Wicher-Dysarz, J. Analysis of the vegetation process in a two-stage reservoir on the basis of satellite imagery–a case study: Radzyny reservoir on the Sama river. Rocznik Ochrona Środowiska. 20 (1), 203–220 (2018). [Google Scholar]
7.Haghnazar, H., Sabbagh, K., Johannesson, K. H., Pourakbar, M. & Aghayani, E. Phytoremediation capability of typha latifolia L. to uptake sediment toxic elements in the largest coastal wetland of the Persian Gulf. Mar. Pollut Bull.188, 114699. 10.1016/j.marpolbul.2023.114699 (2023). [DOI] [PubMed] [Google Scholar]
8.Chen, L., Zhang, X., Zhang, M., Zhu, Y. & Zhuo, R. Removal of heavy-metal pollutants by white rot fungi: Mechanisms, achievements, and perspectives. J. Clean. Prod.354, 131681. 10.1016/j.jclepro.2022.131681( (2022). [Google Scholar]
9.Looi, L. J., Aris, A. Z., Yusoff, F. M., Isa, N. M. & Haris, H. Application of enrichment factor, geoaccumulation index, and ecological risk index in assessing the elemental pollution status of surface sediments. Environ. Geochem. Health. 41, 27–42. 10.1007/s10653-018-0149-1 (2019). [DOI] [PubMed] [Google Scholar]
10.Canlı, O., Çetintürk, K. & Güzel, B. A comprehensive assessment, source input determination and distribution of persistent organic pollutants (POPs) along with heavy metals (HMs) in reservoir lake sediments from Çanakkale province, Türkiye. Environ. Geochem. Health. 45, 3985–4006. 10.1007/s10653-023-01480-4 (2023). [DOI] [PubMed] [Google Scholar]
11.Gunes, G. The change of metal pollution in the water and sediment of the Bartın river in rainy and dry seasons. Environ. Eng. Res.27 (2), 200701. 10.4491/eer.2020.701 (2022). [Google Scholar]
12.Sojka, M., Jaskuła, J., Barabach, J., Ptak, M. & Zhu, S. Heavy metals in lake surface sediments in protected areas in poland: concentration, pollution, ecological risk, sources and Spatial distribution. Sci. Rep.12 (1), 15006. 10.1038/s41598-022-19298-y (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Dendievel, A. M. et al. Key factors influencing metal concentrations in sediments along Western European rivers: A long-term monitoring study (1945–2020). Sci. Total Environ.805, 149778. 10.1016/j.scitotenv.2021.149778 (2022). [DOI] [PubMed] [Google Scholar]
14.Baran, A. et al. Metal contamination in sediments of dam reservoirs: A multi-facetted generic risk assessment. Chemosphere310, 136760. 10.1016/j.chemosphere.2022.136760 (2023). [DOI] [PubMed] [Google Scholar]
15.Nazeer, N., KB, F. S., Sreejisha, U., Mohan, R., Gayathry, O. S., Sudha, A., … Kumar,R. Metal partitioning in sediments and risk assessment of a ramsar site-Cochin estuary,South West India. Reg.Stud. Mar. Sci. 75, 103557, DOI: 10.1016/j.rsma.2024.103557 (2024).
16.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.282, 122113. 10.1016/j.envres.2025.122113 (2025). [DOI] [PubMed] [Google Scholar]
17.Tokatlı, C., Varol, M. & Ustaoğlu, 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.30, 52818–52829. 10.1007/s11356-023-26078-2 (2023). [DOI] [PubMed] [Google Scholar]
18.Bern, C. R., Walton-Day, K. & Naftz, D. L. Improved enrichment factor calculations through principal component analysis: examples from soils near breccia pipe uranium mines, Arizona, USA. Environ. Pollut. 248, 90–100. 10.1016/j.envpol.2019.01.122 (2019). [DOI] [PubMed] [Google Scholar]
19.Castro, M. F. et al. Impact of anthropogenic activities on an urban river through a comprehensive analysis of water and sediments. Environ. Sci. Pollut Res.28, 37754–37767. 10.1007/s11356-021-13349-z (2021). [DOI] [PubMed] [Google Scholar]
20.Abbas, G., Jomaa, S., Bronstert, A. & Rode, M. Downstream changes in riverbank sediment sources and the effect of catchment size. J. Hydrol. : Reg. Stud.46, 101340. 10.1016/j.ejrh.2023.101340 (2023). [Google Scholar]
21.Botle, A., Salgaonkar, S., Tiwari, R. & Barabde, G. Unveiling heavy metal pollution dynamics in sediments of river Ulhas, Maharashtra, india: a comprehensive analysis of anthropogenic influence, pollution indices, and health risk assessment. Environ. Geochem. Health. 46 (10). 10.1007/s10653-024-02208-8 (2024). 419, DOI. [DOI] [PubMed]
22.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.203, 116514. 10.1016/j.marpolbul.2024.116514 (2024). [DOI] [PubMed] [Google Scholar]
23.Rzętała, M. A. et al. Non-Metals and metalloids in bottom sediments as a geoecological indicator of a water body’s suitability for recreational use. Int. J. Environ. Res. Public. Health. 20 (5), 4334. 10.3390/ijerph20054334 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Tomczyk, P., Wdowczyk, A., Wiatkowska, B. & Szymańska-Pulikowska, A. Assessment of heavy metal contamination of agricultural soils in Poland using contamination indicators. Ecol. Indic.156, 111161. 10.1016/j.ecolind.2023.111161 (2023). [Google Scholar]
25.Anyanwu, I. N. et al. Pollution of the Niger delta with total petroleum hydrocarbons, heavy metals and nutrients in relation to seasonal dynamics. Sci. Rep.13 (1), 14079. 10.1038/s41598-023-40995-9 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Geng, N., Bai, Y. & Pan, S. Research on heavy metal release with suspended sediment in Taihu lake under hydrodynamic condition. Environ. Sci. Pollut Res.29 (19), 28588–28597. 10.1007/s11356-021-17666-1 (2022). [DOI] [PubMed] [Google Scholar]
27.Dong, X. et al. Evolution of reservoir quality related to clay coating overgrowths in tight sandstone in the fluvial facies of the southeastern Sichuan basin. Mar. Pet. Geol.165, 106864. 10.1016/j.marpetgeo.2024.106864 (2024). [Google Scholar]
28.Zulti, F., Prihatinningtyas, E., Susanti, E. & Syafutra, H. Scalable wastewater treatment: performance of zeolite and bentonite in a fixed-bed reactor for textile effluents. J Water Process. Eng. 71, 107349. 10.1038/s41598-023-40995-9 (2025). [Google Scholar]
29.Siepak, M. & Sojka, M. Application of multivariate statistical approach to identify trace elements sources in surface waters: A case study of Kowalskie and stare miasto reservoirs, Poland. Environ. Monit. Assess.189, 1–15. 10.1007/s10661-017-6089-x (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Jiang, W., Chu, H., Liu, Y., Chen, B., Feng, Y., Lyu, J., … Hou, W. Distribution of heavy metals in coastal sediments under the influence of multiple factors: A case study from the south coast of an industrialized harbor city (Tangshan, China). Sci. Total Environ.889, 164208, DOI: 10.1016/j.scitotenv.2023.164208 (2023). [DOI] [PubMed]
31.Müller, G. Index of geoaccumulation in sediments of the rhine river. Geol. J.2, 108–118 (1969). [Google Scholar]
32.Bojakowska, I. & Sokołowska, G. Geochemiczne Klasy czystości osadów Wodnych. Przegląd Geol.46, 49–54 (1998). [Google Scholar]
33.Nawrot, N. et al. Trace metal contamination of bottom sediments: a review of assessment measures and geochemical background determination methods. Minerals11 (8), 872. 10.3390/min11080872 (2021). [Google Scholar]
34.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. Mar. Res.33 (1–4), 566–575. 10.1007/BF02414780 (1980). [Google Scholar]
35.Sedeño-Díaz, J. E., López-López, E., Mendoza-Martínez, E., Rodríguez-Romero, A. J. & Morales-García, S. S. Distribution coefficient and metal pollution index in water and sediments: proposal of a new index for ecological risk assessment of metals. Water12 (1), 29. 10.3390/w12010029 (2019). [Google Scholar]
36.Tokatlı, C., Varol, M., Ustaoğlu, 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). Chemosphere340, 139977. 10.1016/j.chemosphere.2023.139977 (2023). [DOI] [PubMed] [Google Scholar]
37.Yüksel, B., Ustaoğlu, F., Tokatli, C. & Islam, M. S. Ecotoxicological risk assessment for sediments of Çavuşlu stream in Giresun, turkey: association between garbage disposal facility and metallic accumulation. Environ. Sci. Pollut Res.29 (12), 17223–17240. 10.1007/s11356-021-17023-2 (2022). [DOI] [PubMed] [Google Scholar]
38.Zhang, G. et al. Heavy metals in wetland soils along a wetland-forming chronosequence in the yellow river delta of china: Levels, sources and toxic risks. Ecol. Indic.69, 331–339. 10.1016/j.ecolind.2016.04.042 (2016). [Google Scholar]
39.Macdonald, D. D., Carr, R. S., Calder, F. D., Long, E. R. & Ingersoll, C. G. Development and evaluation of sediment quality guidelines for Florida coastal waters. Ecotoxicology5, 253–278. 10.1007/BF00118995 (1996). [DOI] [PubMed] [Google Scholar]
40.Li, D. et al. Ecological risk of heavy metals in lake sediments of china: A national-scale integrated analysis. J. Clean. Prod.334, 130206. 10.1016/j.jclepro.2021.130206 (2022). [Google Scholar]
41.Williams, J. A. & Antoine, J. Evaluation of the elemental pollution status of Jamaican surface sediments using enrichment factor, geoaccumulation index, ecological risk and potential ecological risk index. Mar. Pollut Bull.157, 111288. 10.1016/j.marpolbul.2020.111288 (2020). [DOI] [PubMed] [Google Scholar]
42.Hakanson, L. An ecological risk index for aquatic pollution control: A sediment ecological approach. Water Res.14, 975–1001. 10.1016/0043-1354(80)90143-8 (1980). [Google Scholar]
43.Bladé, E. et al. Iber: Herramienta de simulación numérica Del Flujo En ríos. Revista Int. De Métodos Numéricos Para Cálculo Y Diseño En Ingeniería. 30, 1–10. 10.1016/j.rimni.2012.07.004 (2014). [Google Scholar]
44.Sanz-Ramos, M., Bladé, E., Silva-Cancino, N., Salazar, F. & López-Gómez, D. Martínez-Gomariz, E. A probabilistic approach for off-stream reservoir failure flood hazard assessment. Water15 (12), 2202. 10.3390/w15122202 (2023). [Google Scholar]
45.Dysarz, T., Sanz-Ramos, M., Wicher-Dysarz, J. & Jaskuła, J. Potential effects of internal dam-break in stare miasto reservoir in Poland. J. Hydrol. : Reg. Stud.53, 101801. 10.1016/j.ejrh.2024.101801 (2024). [Google Scholar]
46.Wicher-Dysarz, J. Analysis of shear stress and stream power Spatial distributions for detection of operational problems in the stare miasto reservoir. Water11 (4), 691. 10.3390/w11040691 (2019). [Google Scholar]
47.Panda, D. et al. Heavy metals in the sediments of the Chilika Lake, india: contamination, source and toxicity analysis. Water Air Soil. Pollut. 235 (6), 415. 10.1007/s11270-024-07226-x (2024). [Google Scholar]
48.Gunawardena, J., Ziyath, A. M., Egodawatta, P., Ayoko, G. A. & Goonetilleke, A. Sources and transport pathways of common heavy metals to urban road surfaces. Ecol. Eng.77, 98–102. 10.1016/j.ecoleng.2015.01.023 (2015). [Google Scholar]
49.Nawrot, N., Wojciechowska, E., Rezania, S., Walkusz-Miotk, J. & Pazdro, K. The effects of urban vehicle traffic on heavy metal contamination in road sweeping waste and bottom sediments of retention tanks. Sci. Total Environ.749, 141511. 10.1016/j.scitotenv.2020.141511 (2020). [DOI] [PubMed] [Google Scholar]
50.Gao, S. et al. Heavy metals in road-deposited sediment and runoff in urban and intercity expressways. Transp. Saf. Environ.4 (1). 10.1093/tse/tdab030 (2021). tdab030, DOI.
51.Zhu, H. et al. The Spatial and vertical distribution of heavy metal contamination in sediments of the three Gorges reservoir determined by anti-seasonal flow regulation. Sci. Total Environ.664, 79–88. 10.1016/j.scitotenv.2019.02.016 (2019). [DOI] [PubMed] [Google Scholar]
52.Lv, H., Chen, J. & Chen, R. Hydraulic reconstruction of paleolandslide-dammed lake outburst flood using water–sediment mixture flow modeling: a case study of Xuelongnang, upstream Jinsha river. Water16 (24), 3713. 10.3390/w16243713 (2024). [Google Scholar]
53.Mohajane, C. & Manjoro, M. Sediment-associated heavy metal contamination and potential ecological risk along an urban river in South Africa. Heliyon8, e12499. 10.1016/j.heliyon.2022.e12499 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Çelebi, A., Şengörür, B. & Kløve, B. Seasonal and Spatial variations of metals in Melen watershed Groundwater, Turkey. CLEAN–Soil Air Water. 43 (5), 739–745. 10.1002/clen.201300774 (2015). [Google Scholar]
55.Tokatlı, C., Ustaoğlu, F., Yazman, M. M. & Yüksel, B. Where rivers Meet the sea: source fingerprinting and health risk mapping of potentially hazardous elements in sediments from the Çanakkale Strait basin (Türkiye). Mar. Pollut Bull.222, 118626. 10.1016/j.marpolbul.2025.118626 (2026). [DOI] [PubMed] [Google Scholar]
56.Cabral, J. B. P. et al. Sediments of hydropower plant water reservoirs contaminated with potentially toxic elements as indicators of environmental risk for river basins. Water16 (19), 2733. 10.3390/w16192733 (2024). [Google Scholar]
57.Yang, Z., Zou, Z., Tan, S. & Yan, H. Assessment of historical heavy metal (loid) s contamination records reveals dominant control of grain size effect on the sedimentary profiles from the North yellow sea. Environ. Pollut. 385, 127078. 10.1016/j.envpol.2025.127078 (2025). [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(120.1KB, docx)}

Supplementary Material 2^{(15.1KB, docx)}

Data Availability Statement

Data will be available upon request, please contact with first author of the paper.

[CR1] 1.Biswas, R. R., Sharma, R. & Gyasi-Agyei, Y. Adaptation to climate change: A study on regional urban water management and planning practice. J. Clean. Prod.355, 131643. 10.1016/j.jclepro.2022.131643 (2022). [Google Scholar]

[CR2] 2.Bytyqi, V. & Agaj, T. Spatio-temporal distribution of renewable freshwater resources and their availability in Kosovo an analysis from the Eastern region. J. Bulg. Geogr. Soc.50, 35–52. 10.3897/jbgs.e115814 (2024). [Google Scholar]

[CR3] 3.Dąbrowska, J. et al. Between flood and drought: how cities are facing water surplus and scarcity. J. Environ. Manage.345, 118557. 10.1016/j.jenvman.2023.118557 (2023). [DOI] [PubMed] [Google Scholar]

[CR4] 4.Haro-Monteagudo, D., Palazón, L., Zoumides, C. & Beguería, S. Optimal implementation of climate change adaptation measures to ensure long-term sustainability on large irrigation systems. Water Resour. Manage.37 (8), 2909–2924. 10.1007/s11269-022-03225-x (2023). [Google Scholar]

[CR5] 5.Pulley, S. & Collins, A. L. Using the colour of recent overbank sediment deposits in two large catchments to determine sediment sources for targeting mitigation of catchment-specific management issues. J. Environ. Manage.336, 117657. 10.1016/j.jenvman.2023.117657 (2023). [DOI] [PubMed] [Google Scholar]

[CR6] 6.Jaskuła, J., Sojka, M. & Wicher-Dysarz, J. Analysis of the vegetation process in a two-stage reservoir on the basis of satellite imagery–a case study: Radzyny reservoir on the Sama river. Rocznik Ochrona Środowiska. 20 (1), 203–220 (2018). [Google Scholar]

[CR7] 7.Haghnazar, H., Sabbagh, K., Johannesson, K. H., Pourakbar, M. & Aghayani, E. Phytoremediation capability of typha latifolia L. to uptake sediment toxic elements in the largest coastal wetland of the Persian Gulf. Mar. Pollut Bull.188, 114699. 10.1016/j.marpolbul.2023.114699 (2023). [DOI] [PubMed] [Google Scholar]

[CR8] 8.Chen, L., Zhang, X., Zhang, M., Zhu, Y. & Zhuo, R. Removal of heavy-metal pollutants by white rot fungi: Mechanisms, achievements, and perspectives. J. Clean. Prod.354, 131681. 10.1016/j.jclepro.2022.131681( (2022). [Google Scholar]

[CR9] 9.Looi, L. J., Aris, A. Z., Yusoff, F. M., Isa, N. M. & Haris, H. Application of enrichment factor, geoaccumulation index, and ecological risk index in assessing the elemental pollution status of surface sediments. Environ. Geochem. Health. 41, 27–42. 10.1007/s10653-018-0149-1 (2019). [DOI] [PubMed] [Google Scholar]

[CR10] 10.Canlı, O., Çetintürk, K. & Güzel, B. A comprehensive assessment, source input determination and distribution of persistent organic pollutants (POPs) along with heavy metals (HMs) in reservoir lake sediments from Çanakkale province, Türkiye. Environ. Geochem. Health. 45, 3985–4006. 10.1007/s10653-023-01480-4 (2023). [DOI] [PubMed] [Google Scholar]

[CR11] 11.Gunes, G. The change of metal pollution in the water and sediment of the Bartın river in rainy and dry seasons. Environ. Eng. Res.27 (2), 200701. 10.4491/eer.2020.701 (2022). [Google Scholar]

[CR12] 12.Sojka, M., Jaskuła, J., Barabach, J., Ptak, M. & Zhu, S. Heavy metals in lake surface sediments in protected areas in poland: concentration, pollution, ecological risk, sources and Spatial distribution. Sci. Rep.12 (1), 15006. 10.1038/s41598-022-19298-y (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.Dendievel, A. M. et al. Key factors influencing metal concentrations in sediments along Western European rivers: A long-term monitoring study (1945–2020). Sci. Total Environ.805, 149778. 10.1016/j.scitotenv.2021.149778 (2022). [DOI] [PubMed] [Google Scholar]

[CR14] 14.Baran, A. et al. Metal contamination in sediments of dam reservoirs: A multi-facetted generic risk assessment. Chemosphere310, 136760. 10.1016/j.chemosphere.2022.136760 (2023). [DOI] [PubMed] [Google Scholar]

[CR15] 15.Nazeer, N., KB, F. S., Sreejisha, U., Mohan, R., Gayathry, O. S., Sudha, A., … Kumar,R. Metal partitioning in sediments and risk assessment of a ramsar site-Cochin estuary,South West India. Reg.Stud. Mar. Sci. 75, 103557, DOI: 10.1016/j.rsma.2024.103557 (2024).

[CR16] 16.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.282, 122113. 10.1016/j.envres.2025.122113 (2025). [DOI] [PubMed] [Google Scholar]

[CR17] 17.Tokatlı, C., Varol, M. & Ustaoğlu, 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.30, 52818–52829. 10.1007/s11356-023-26078-2 (2023). [DOI] [PubMed] [Google Scholar]

[CR18] 18.Bern, C. R., Walton-Day, K. & Naftz, D. L. Improved enrichment factor calculations through principal component analysis: examples from soils near breccia pipe uranium mines, Arizona, USA. Environ. Pollut. 248, 90–100. 10.1016/j.envpol.2019.01.122 (2019). [DOI] [PubMed] [Google Scholar]

[CR19] 19.Castro, M. F. et al. Impact of anthropogenic activities on an urban river through a comprehensive analysis of water and sediments. Environ. Sci. Pollut Res.28, 37754–37767. 10.1007/s11356-021-13349-z (2021). [DOI] [PubMed] [Google Scholar]

[CR20] 20.Abbas, G., Jomaa, S., Bronstert, A. & Rode, M. Downstream changes in riverbank sediment sources and the effect of catchment size. J. Hydrol. : Reg. Stud.46, 101340. 10.1016/j.ejrh.2023.101340 (2023). [Google Scholar]

[CR21] 21.Botle, A., Salgaonkar, S., Tiwari, R. & Barabde, G. Unveiling heavy metal pollution dynamics in sediments of river Ulhas, Maharashtra, india: a comprehensive analysis of anthropogenic influence, pollution indices, and health risk assessment. Environ. Geochem. Health. 46 (10). 10.1007/s10653-024-02208-8 (2024). 419, DOI. [DOI] [PubMed]

[CR22] 22.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.203, 116514. 10.1016/j.marpolbul.2024.116514 (2024). [DOI] [PubMed] [Google Scholar]

[CR23] 23.Rzętała, M. A. et al. Non-Metals and metalloids in bottom sediments as a geoecological indicator of a water body’s suitability for recreational use. Int. J. Environ. Res. Public. Health. 20 (5), 4334. 10.3390/ijerph20054334 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 24.Tomczyk, P., Wdowczyk, A., Wiatkowska, B. & Szymańska-Pulikowska, A. Assessment of heavy metal contamination of agricultural soils in Poland using contamination indicators. Ecol. Indic.156, 111161. 10.1016/j.ecolind.2023.111161 (2023). [Google Scholar]

[CR25] 25.Anyanwu, I. N. et al. Pollution of the Niger delta with total petroleum hydrocarbons, heavy metals and nutrients in relation to seasonal dynamics. Sci. Rep.13 (1), 14079. 10.1038/s41598-023-40995-9 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Geng, N., Bai, Y. & Pan, S. Research on heavy metal release with suspended sediment in Taihu lake under hydrodynamic condition. Environ. Sci. Pollut Res.29 (19), 28588–28597. 10.1007/s11356-021-17666-1 (2022). [DOI] [PubMed] [Google Scholar]

[CR27] 27.Dong, X. et al. Evolution of reservoir quality related to clay coating overgrowths in tight sandstone in the fluvial facies of the southeastern Sichuan basin. Mar. Pet. Geol.165, 106864. 10.1016/j.marpetgeo.2024.106864 (2024). [Google Scholar]

[CR28] 28.Zulti, F., Prihatinningtyas, E., Susanti, E. & Syafutra, H. Scalable wastewater treatment: performance of zeolite and bentonite in a fixed-bed reactor for textile effluents. J Water Process. Eng. 71, 107349. 10.1038/s41598-023-40995-9 (2025). [Google Scholar]

[CR29] 29.Siepak, M. & Sojka, M. Application of multivariate statistical approach to identify trace elements sources in surface waters: A case study of Kowalskie and stare miasto reservoirs, Poland. Environ. Monit. Assess.189, 1–15. 10.1007/s10661-017-6089-x (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Jiang, W., Chu, H., Liu, Y., Chen, B., Feng, Y., Lyu, J., … Hou, W. Distribution of heavy metals in coastal sediments under the influence of multiple factors: A case study from the south coast of an industrialized harbor city (Tangshan, China). Sci. Total Environ.889, 164208, DOI: 10.1016/j.scitotenv.2023.164208 (2023). [DOI] [PubMed]

[CR31] 31.Müller, G. Index of geoaccumulation in sediments of the rhine river. Geol. J.2, 108–118 (1969). [Google Scholar]

[CR32] 32.Bojakowska, I. & Sokołowska, G. Geochemiczne Klasy czystości osadów Wodnych. Przegląd Geol.46, 49–54 (1998). [Google Scholar]

[CR33] 33.Nawrot, N. et al. Trace metal contamination of bottom sediments: a review of assessment measures and geochemical background determination methods. Minerals11 (8), 872. 10.3390/min11080872 (2021). [Google Scholar]

[CR34] 34.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. Mar. Res.33 (1–4), 566–575. 10.1007/BF02414780 (1980). [Google Scholar]

[CR35] 35.Sedeño-Díaz, J. E., López-López, E., Mendoza-Martínez, E., Rodríguez-Romero, A. J. & Morales-García, S. S. Distribution coefficient and metal pollution index in water and sediments: proposal of a new index for ecological risk assessment of metals. Water12 (1), 29. 10.3390/w12010029 (2019). [Google Scholar]

[CR36] 36.Tokatlı, C., Varol, M., Ustaoğlu, 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). Chemosphere340, 139977. 10.1016/j.chemosphere.2023.139977 (2023). [DOI] [PubMed] [Google Scholar]

[CR37] 37.Yüksel, B., Ustaoğlu, F., Tokatli, C. & Islam, M. S. Ecotoxicological risk assessment for sediments of Çavuşlu stream in Giresun, turkey: association between garbage disposal facility and metallic accumulation. Environ. Sci. Pollut Res.29 (12), 17223–17240. 10.1007/s11356-021-17023-2 (2022). [DOI] [PubMed] [Google Scholar]

[CR38] 38.Zhang, G. et al. Heavy metals in wetland soils along a wetland-forming chronosequence in the yellow river delta of china: Levels, sources and toxic risks. Ecol. Indic.69, 331–339. 10.1016/j.ecolind.2016.04.042 (2016). [Google Scholar]

[CR39] 39.Macdonald, D. D., Carr, R. S., Calder, F. D., Long, E. R. & Ingersoll, C. G. Development and evaluation of sediment quality guidelines for Florida coastal waters. Ecotoxicology5, 253–278. 10.1007/BF00118995 (1996). [DOI] [PubMed] [Google Scholar]

[CR40] 40.Li, D. et al. Ecological risk of heavy metals in lake sediments of china: A national-scale integrated analysis. J. Clean. Prod.334, 130206. 10.1016/j.jclepro.2021.130206 (2022). [Google Scholar]

[CR41] 41.Williams, J. A. & Antoine, J. Evaluation of the elemental pollution status of Jamaican surface sediments using enrichment factor, geoaccumulation index, ecological risk and potential ecological risk index. Mar. Pollut Bull.157, 111288. 10.1016/j.marpolbul.2020.111288 (2020). [DOI] [PubMed] [Google Scholar]

[CR42] 42.Hakanson, L. An ecological risk index for aquatic pollution control: A sediment ecological approach. Water Res.14, 975–1001. 10.1016/0043-1354(80)90143-8 (1980). [Google Scholar]

[CR43] 43.Bladé, E. et al. Iber: Herramienta de simulación numérica Del Flujo En ríos. Revista Int. De Métodos Numéricos Para Cálculo Y Diseño En Ingeniería. 30, 1–10. 10.1016/j.rimni.2012.07.004 (2014). [Google Scholar]

[CR44] 44.Sanz-Ramos, M., Bladé, E., Silva-Cancino, N., Salazar, F. & López-Gómez, D. Martínez-Gomariz, E. A probabilistic approach for off-stream reservoir failure flood hazard assessment. Water15 (12), 2202. 10.3390/w15122202 (2023). [Google Scholar]

[CR45] 45.Dysarz, T., Sanz-Ramos, M., Wicher-Dysarz, J. & Jaskuła, J. Potential effects of internal dam-break in stare miasto reservoir in Poland. J. Hydrol. : Reg. Stud.53, 101801. 10.1016/j.ejrh.2024.101801 (2024). [Google Scholar]

[CR46] 46.Wicher-Dysarz, J. Analysis of shear stress and stream power Spatial distributions for detection of operational problems in the stare miasto reservoir. Water11 (4), 691. 10.3390/w11040691 (2019). [Google Scholar]

[CR47] 47.Panda, D. et al. Heavy metals in the sediments of the Chilika Lake, india: contamination, source and toxicity analysis. Water Air Soil. Pollut. 235 (6), 415. 10.1007/s11270-024-07226-x (2024). [Google Scholar]

[CR48] 48.Gunawardena, J., Ziyath, A. M., Egodawatta, P., Ayoko, G. A. & Goonetilleke, A. Sources and transport pathways of common heavy metals to urban road surfaces. Ecol. Eng.77, 98–102. 10.1016/j.ecoleng.2015.01.023 (2015). [Google Scholar]

[CR49] 49.Nawrot, N., Wojciechowska, E., Rezania, S., Walkusz-Miotk, J. & Pazdro, K. The effects of urban vehicle traffic on heavy metal contamination in road sweeping waste and bottom sediments of retention tanks. Sci. Total Environ.749, 141511. 10.1016/j.scitotenv.2020.141511 (2020). [DOI] [PubMed] [Google Scholar]

[CR50] 50.Gao, S. et al. Heavy metals in road-deposited sediment and runoff in urban and intercity expressways. Transp. Saf. Environ.4 (1). 10.1093/tse/tdab030 (2021). tdab030, DOI.

[CR51] 51.Zhu, H. et al. The Spatial and vertical distribution of heavy metal contamination in sediments of the three Gorges reservoir determined by anti-seasonal flow regulation. Sci. Total Environ.664, 79–88. 10.1016/j.scitotenv.2019.02.016 (2019). [DOI] [PubMed] [Google Scholar]

[CR52] 52.Lv, H., Chen, J. & Chen, R. Hydraulic reconstruction of paleolandslide-dammed lake outburst flood using water–sediment mixture flow modeling: a case study of Xuelongnang, upstream Jinsha river. Water16 (24), 3713. 10.3390/w16243713 (2024). [Google Scholar]

[CR53] 53.Mohajane, C. & Manjoro, M. Sediment-associated heavy metal contamination and potential ecological risk along an urban river in South Africa. Heliyon8, e12499. 10.1016/j.heliyon.2022.e12499 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR54] 54.Çelebi, A., Şengörür, B. & Kløve, B. Seasonal and Spatial variations of metals in Melen watershed Groundwater, Turkey. CLEAN–Soil Air Water. 43 (5), 739–745. 10.1002/clen.201300774 (2015). [Google Scholar]

[CR55] 55.Tokatlı, C., Ustaoğlu, F., Yazman, M. M. & Yüksel, B. Where rivers Meet the sea: source fingerprinting and health risk mapping of potentially hazardous elements in sediments from the Çanakkale Strait basin (Türkiye). Mar. Pollut Bull.222, 118626. 10.1016/j.marpolbul.2025.118626 (2026). [DOI] [PubMed] [Google Scholar]

[CR56] 56.Cabral, J. B. P. et al. Sediments of hydropower plant water reservoirs contaminated with potentially toxic elements as indicators of environmental risk for river basins. Water16 (19), 2733. 10.3390/w16192733 (2024). [Google Scholar]

[CR57] 57.Yang, Z., Zou, Z., Tan, S. & Yan, H. Assessment of historical heavy metal (loid) s contamination records reveals dominant control of grain size effect on the sedimentary profiles from the North yellow sea. Environ. Pollut. 385, 127078. 10.1016/j.envpol.2025.127078 (2025). [DOI] [PubMed] [Google Scholar]

PERMALINK

Role of hydraulic parameters in the concentration and spatial distribution of heavy metals in sediments in a two-stage reservoir

Joanna Jaskuła

Tomasz Dysarz

Joanna Wicher-Dysarz

Mariusz Sojka

Tropikë Agaj

Marcos Sanz-Ramos

Marcin Siepak

Abstract

Supplementary Information

Introduction

Materials and methods

Study area

Fig. 1.

Materials

Methods

Field measurements and laboratory analyses

Geochemical indices

Ecological risk assessment

Hydraulic modeling

Sediments volume

Table 1.

Statistical methods

Results

HM concentrations

Table 2.

Fig. 2.

Geochemical indices

Fig. 3.

Hydraulic modeling

Fig. 4.

Sediment volume

Fig. 5.

Statistical analyses

Fig. 6.

Table 3.

Discussion

Conclusion

Supplementary Information

Author contributions

Data availability

Declarations

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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