Distribution, contamination and source discrimination of heavy metals in sediments from dam reservoir at Changsha city along the Xiangjiang River, China

Distribution, contamination and source discrimination of heavy metals in sediments from dam reservoir at Changsha city along the Xiangjiang River, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distribution, contamination and source discrimination of heavy metals in sediments from dam reservoir at Changsha city along the Xiangjiang River, China Xia Yang, Bo Peng, Sicheng Wu, Nengqiu Wu, Hongjie Hu, Xianjia Du, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7302369/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2025 Read the published version in Environmental Geochemistry and Health → Version 1 posted 11 You are reading this latest preprint version Abstract Many statistics methods are used to explore heavy metals distribution, contamination, and human health risks of the Xiangjiang dam sediment. Results showed that dam sediments (DS) and pre-dam fluvial sediments(FS) had similar major and some trace element compositions. Meanwhile, the distribution differences of trace metals Ni, Mn, Zn, Cd, Cu and Pb was characterized; for which Ni had distinctly higher (up to 7699.5 mg/kg), and others had lower concentrations in the DS than FS. Heavy metals contamination in DS arrived at high degree for Ni (average I Geo of 10.2) and Cd (average I Geo of 3.2), and low to moderate for Mn, Zn, Cu, and Pb (1.52 < I Geo 1), and the health risk for child is higher than adult. The non carcinogenic risk of Ni for adult (HI = 3.2389) and child (HI = 4.9751) are mainly in YP9. The non carcinogenic risk of Cd to adult (HI = 1.2579) and child (HI = 2.4587) is mainly located in SC1. Source discrimination study showed that metals Mn, Zn, Cd, Cu and Pb in the DS were from mining activities, while Ni was from the waste discharges like agricultural, catering within the reservoir. Protection for metals (especially Ni, Cd) contamination in the dam reservoir should pay a great attention to the anthropogenic activities both in upper river areas and within the dam reservoir. Heavy metal contamination Dam sediment Mining activity Ecological risk human health risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction The dam construction along river all over the world has expanded rapidly with the desire for clean electricity, irrigation, flood control, and urban development (Zhao et al., 2022 ; Guo et al., 2023 ). Dam reservoir along river are typical sites where sediments accumulation is favoured (Vukovic et al., 2014 ), allowing their deposition and accumulation of heavy metals in sediments from both natural processes and anthropogenic activities (e.g., Fonseca et al., 2016 ; Peng et al., 2022a , b ). It is suggested that over 90% of heavy metals in aquatic ecosystems can be adsorbed or precipitated by sediments (Zhao et al., 2022 ). Thus, the concentrations of heavy metals in dam sediments increase after the construction, as observed in many dams such as the famous Three Gorges Reservoir in China (Bing et al., 2016 ; Gao et al., 2019 ; Lin et al., 2020 ), Vaussaire dam in France (Frémion et al., 2016 ), dam lakes in South Egypt (Darwish and Pöllmann, 2018 ), and dam reservoir along the Ohře River of Czech Republic (Matys Grygar et al., 2018). This accumulation of heavy metals in dam sediments then may have negative impacts on the aquatic ecological environment of reservoir, which affects aquatic food safety and environmental quality (Guo et al., 2023 ; Liu et al., 2024 ). Therefore, the heavy metal contamination in dam sediments has been a particularly important issue for the environmental protection and ecological safty of river water (e.g., Vergilio et al., 2021 ; Almeida et al., 2024 ). Dam sediments can deposit and concentrate heavy metals through dissolution, precipitation, and sorption with clays, organic matter (OM) and Fe/Mn oxides in reservior sediments, and the distribution of heavy metals in dam sediments is generally regulated by sediment composition (e.g., Vukovic et al., 2014 ; Zhao et al., 2022 ). For example, the Three Gorges Dam caused upstream sedimentary accumulation of heavy metals to be higher nearer to the dam than in the upper reaches, and pollutant content was sharply lower below the dam due to regulation of the spatial variation in sediment particle size (e.g., Xu et al., 2019 ; Wang et al., 2022 ; Guo et al., 2023 ), and OM and Fe/Mn oxide contents (e.g., Guo et al., 2023 ; Cieśla and Gruca-Rokosz, 2024 ). Thus, the distribution of heavy metals in dam sediments and its association with the sediment grain size, OM and Fe/Mn oxide contents (Frémion et al., 2016 ; Nguyen et al., 2022 ), and carbon, nitrogen, phosphorus, polycyclic aromatic hydrocarbons, and oxygenated PAHs contents (Guo et al., 2023 ; Dueñas-Moreno et al., 2024 ) have frequently been studied. Although the heavy metal contamination in dam sediments has been assessed (Almeida et al., 2024 ), and its impact to the concentrations of heavy metals (e.g., As, Fe, Cd, Ni, and Zn) in reservoir water (Vukovic et al., 2014 ; Liu et al., 2023 ) and (Cu, Fe, Zn and Hg) in aquatic biota (Sang et al., 2019 ; Zhao et al., 2021 ; Cieśla and Gruca-Rokosz, 2024 ) has particuarly been elucidated, there is a lack of study focused on geochemistry of heavy metals in sediments from the dam reservoir trapped (post-dam) and those deposited in river bed on site before dam constructed (pre-dam) ( Zhu et al., 2019 ; Zhang et al., 2022). An effort of such a study is uncommon because of its cost and sampling opportunity (Pacheco et al., 2023 ). As dam reservoir deposites may provide valuable sedimentary archives of heavy metals from both natural processes and anthropogenic activities (Yang et al., 2018 ), and heavy metals may a threat to water and human health (Zhu et al., 2019 ; Almeida et al., 2024 , Sreelesh et al., 2025); so it is essential to study the geochemistry of both the post-dam sediments (DS) and pre-dam fluvial sediments (FS) from a river to understand dam-induced changes in distribution and contamination of heavy metals in sediments. The Xiangjiang River, often referred to as the mother river of Hunan province, runs through the Changsha-Zhuzhou-Xiangtan urban area, a central hub for the province's economy, politics, and culture. Moreover, the dam is relatively closed to the urban area of Changsha. So the ecological environmental problems in this reservoir area are worthy of in-depth research. According to the average sedimentation rate of 2 cm/a in the downstream section of the Xiangjiang River (Audry et al., 2004), approximately 20 cm thick new sediments (DS, post-dam) had been deposited in the reservoir area during the past 10 year. The government had implemented pollution prevention and control measures in Xiangjiang River Basin over 10 year. But there are few reports on heavy metal pollution in Changsha dam sediment. Therefore, it is possible to take a compare study on geochemistry of the post- and pre-dam sediments on site from this river (Fig. 1c). The present study contributes to this with a purpose to understand the distribution and contamination of heavy metals in the DS by (1) unearthing the geochemical variations between the DS and FS; (2) discriminating the trace metal sources in the DS using geochemical indicators on identifying natural sources from anthropogenic inputs; and (3) evaluating the contamination, and human health risk of heavy metals in the dam sediments. 2. Materials and methods 2.1 Study area Changsha City is situated in the northeastern part of Hunan Province (Fig. 1a), 27°55'N to 28°41'N, 111°54'E to 114°15'E, its area has 1.18×104 km 2 . The rock layeFS exposed throughout the Xiangjiang River basin are generally well-preserved, and granite rocks are widely distributed (Peng et al., 2011 ), but the research area is mainly composed of Quaternary sediments. The mudstone, red sandstone, and siltstone are the mainly origin rocks of sediment. This area is marked by a typical subtropical monsoon climate, with an annual average temperature ranging from 16.8 to 17.3°C and yearly precipitation between 1359 and 1553 mm. Before the implementation of pollution prevention and control projects, the upstream areas of the study area, including Shuikoushan in Hengyang, Xiawan bay in Zhuzhou(ZMP), Yuetang in Xiangtan(XSC), and Zhubu bay(ZCD), were important industrial and mining areas within the Xiangjiang River Basin (Li et al., 2022 ). Previously, industrial and mining enterprises had a long history of development, and a considerable quantity of industrial waste was released into the Xiangjiang River, causing the sediment in Changsha section to receive various pollutants from the upstream basin over an extended period (Chai et al., 2016 ; Fang et al., 2019 ). After the dam construction, heavy metals may be more easily enriched in reservoir area (Bing et al., 2016 ). 2.2 Sampling and sediment samples The Xiangjiang Changsha comprehensive dam is located in Caijiazhou, Wangcheng District, Changsha City (Fig. 1b). The ninth level of a cascade dam in Xiangjiang River. The main task is to secure the production and domestic water supply for the urban agglomeration of Changsha, Zhuzhou, and Xiangtan, adapt to the construction of waterfront landscape belts, enhance the navigation conditions of the waterway in the Changsha-Zhuzhou-Xiangtan stretch, and take into account functions such as power generation. The sampling of dam sediments (DS) was completed in November 2022. This work used a grab type mud collector to collect 44 sediment samples, and they are named YP (Yinpenling Bridge), FY (Fuyuan Road Bridge), SW (Wangcheng), and JG(Jing gang) respectively. The specific location distribution is shown in Fig. 1b. To obtain more representative surface sediment, a sample was taken every 200 m at each sampling point. All samples gathered are stored in sealed plastic bags and transported to the laboratory for analysis. The sampling of river sediment (FS, pre-dam) was completed by our research group in November 2010, with specific methods referring to Peng et al. ( 2011 ). The newly collected DS sediment samples are about 20 cm thick, all of which are brownish yellow silty silt sediments (Fig. 1c). The upper layer of FS sediment (0-20cm) has a lighter color, mainly consisting of brownish yellow and light-yellow silty silt layeFS. The lower layer (> 50cm) is brown and black silty silt, with no mica fragments or plant branches observed (Fang et al., 2019 ). 2.3 Analyses of elements The analysis of the primary elements in the DS and FS sediments from the Xiangjiang reservoir area was conducted using a PW2404 X-ray fluorescence analyzer (XRF) located at the State Key Laboratory of Isotope Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. The chemical treatment of samples, instrument working conditions, analysis accuracy, error and standard samples were referred to the relevant photographic literature (Fang et al., 2021 ). Heavy metal concentrations in DS sediment samples were analyzed at the Key Laboratory of Isotopic Geochemistry, part of the Chinese Academy of Sciences. Heavy metals in dissolved samples were measured over a broad concentration range using an Elan 6000 inductively coupled plasma mass spectrometer (ICP-MS). For detailed analysis methods, please refer to Fang et al. ( 2023 ). The 1M HCl extractants from samples labeled with S and R suffixes were analyzed using a Perkin Elmer Optima 5300 ICP-MS at the Future Industries Institute, University of South Australia. The results generally deviated by no more than ± 7% from the certified values, with an analytical accuracy exceeding 5% for the measurements. 2.4 Enrichment, contamination and ecilogical risk assessmenrt methods 2.4.1 Enrichment of trace metals The enrichment degree of heavy metals was assessed using the enrichment factor (EF), calculated by Eq. (3). This factor represents the ratio of the double-normalized target metal A to the reference element A Ref in a sample, compared to the background concentration. The definition of EF aligns with the assumption that metal A and A Ref in a sample exist within finely crystalline secondary phases (such as silt and clay), which are diluted by quartz particles, and are released from A and ARef (Bing et al., 2016 ). So, this article selects Al as the reference element (Peng et al., 2011 ; Fang et al., 2019 ): EF=(X/A)sample/(X/A)background (1) 2.4.2 Assessment of heavy metal contamination and ecological risk The degree of heavy metal contamination in sediments was assessed using the Geoaccumulation index (I Geo ) by eliminating the influence of geological contribution. For this, the background concentration of each metal and a correction coefficient K were included in the assessment, expressed as Eq. (2): I Geo = Log 2 [C M / (K × B M )] (2) where C M is the concentration of the evaluated metal M in a sample and B M is the LBV of that metal. Thus, the most important step for the assessment is the definition of K. Here, K value was determined by normalizing the concentration of Al 2 O 3 in the samples to the LBV (12.8%) (Zhao et al., 2021 ), as shown in Eq. (3): K = (C Al2O3 ) sample / (C Al2O3 ) LBV (3) 2.4.3 Assessment model of human health risk We use the health risk model proposed by USEPA (USEPA, 2014) to evaluate the non carcinogenic risks of heavy metals (Cd, Ni, Cu, Pb, Zn, Mn) to humans in sediments of the Xiangjiang Reservoir area: HI = HQ ing + HQ der + HQ inh (4) In the above equation, HQ ing , HQ der , and HQ nah represent the non carcinogenic risk quotient for ingestion, skin contact, and inhalation, respectively; HI represents the non carcinogenic risk factor value (Jafarabadi et al., 2017 ; Sreelesh et al., 2025). 2.5 Source analysis of heavy metals (APCS-MLR model) The APCS-MLR model works by deriving the rotation factor loading matrix and eigenvalues through principal component analysis (PCA),and calculating the principal factor eigenvector. To assess the contribution of identified pollution sources to the substances in the receptor, multiple linear regression (MLR) was performed using feature vector and absolute principal component scores (APCS) obtained from standardized data (Lv et al.,2019; Peng et al., 2024). The formula is 2.6 Statistical analysis Pearson correlation analysis and Principal component analysis (PCA) are employed to examine the relationships between major and heavy metals. Additionally, PCA and derivation techniques can help identify potential sources of heavy metal pollution in sediments(Fang et al., 2019 ). A KMO value greater than 0.5 and Bartlett tests (P < 0.001) serve as criteria to assess the validity of the PCA results and the variance matrix for heavy metals in the sediments (Chai et al., 2016 ; Zhuang et al., 2018 ). SPSS 18.0 for Windows was used to perform all statistical analyses. The Shapiro test was applied to assess the normality of the distribution for major elements and heavy metals, including concentrations, enrichment coefficients, and ground accumulation index for DS/FS sediment (Zhuang et al., 2018 ). The equality of variance and the form of the t-statistic were determined using the f-test. For elements that are not normally distributed, the non-parametric Mann-Whitney test was applied. 3. Results and discussion 3.1 Distribution of major and trace elements in sediments Concentrations of major and trace elements in the DS and FS were reported in Supplemantary STable 1, in which relative data of the UCC (Gao et al., 1999 ), NASC (Gromet et al., 1984 ), HGR(Yan et al., 1997 ), China soils (CAS, Yan et al., 1997 ), Yangtze river sediments (YZ, Yan et a., 1997), and XJR (Zhao et al., 2021 ) were included for comparison. The distribution features of major and trace elements in sediments are summarized below. 3.1.1 Major elements Concentrations of major elements SiO 2 , TiO 2 , Al 2 O 3 , Fe 2 O 3 , MnO, MgO, CaO, K 2 O, Na 2 O, P 2 O 5 , and LOI in the DS (n = 52) were around 69.5, 0.68, 12.59, 5.61, 0.23, 1.03, 0.91, 2.2, 0.44, 0.17, and 6.52 (wt.%) respectively, with Cv (coefficient of variation) < 0.2. Such a major element composition of the DS was comparable to that of the UCC (Gao et al., 1999 ), NASC (Gromet et al., 1984 ), CAS (Yan et al., 1997 ), YZ (Yan et al., 1997 ), and XJR (Zhao et al., 2021 ), as shown by plots of SiO 2 % vs. oxide concentrations (Fig. 2). Also, Fig. 2 displayed that SiO 2 concentrations were negatively linarly correlated to those of TiO 2 (r 2 = 0.53), Al 2 O 3 (r 2 = 0.95), Fe 2 O 3 (r 2 = 0.82), MnO (r 2 = 0.62), MgO (r 2 = 0.52), P 2 O 5 (r 2 = 0.56) and LOI (r 2 = 0.94) (Fig. 2a-e, I, j), and positively to Na 2 O (r 2 = 0.71) (Fig. 2g), showing significant grain size effect on element concentration (Fang et al., 2021 ). Concentrations of mobile elements CaO (around 0.91 wt.%), K 2 O (2.2 wt.%), and Na 2 O (0.44 wt.%) in the DS were significantly lower than that of the UCC (Gao et al., 1999 ), NASC (Gromet et al., 1984 ), and HGR (Yan et al., 1997 ) (STable 1, Fig. 2f-h), suggesting lost of these elements through leaching during source rock weathering (Sharma et al., 2013 ). This rock weathering was in moderate to high degree as indicated by CIA (Nesbitt and Young, 1982 ) values ranging from 67.5 to 82.4 (Fig. 3a), and it represented one of the major natural processes that determined the element composition of the DS (Chen et al., 2021 ; Wu et al., 2021 ). In general, the hydrological differentiation and chemical weathering processes that operate on river sediments lead to an increase in quartz at the expense of feldspar, mafic minerals, and lithic fragments in the river sediments (Singh 2009 ), and then result in an increase in the sediment maturity (Singh 2009 ; Wu et al., 2021 ). Thus, the plot of log(SiO 2 /Al 2 O 3 ) vs. log(Fe 2 O 3 /K 2 O) (Singh 2009 ) was used to characterize the chemical maturity of the DS, it displayed that the DS samples occupied a field from litharenite through wacke to shale (Fig. 3b), showing the chemical immaturity of the DS. This chemical immaturity can also be indicated by the ICV values (0.82–1.25) that were higher than those of matured clays (0.03–0.78) (Mclennan et al., 1993 ). Therefore, the DS might have suffered from the upper river source lithological differences (Fig. 1a) after less well hydrological sorting (Wu et al., 2021 ; Chen et al., 2021 ). More importantly, the FS had major element compositions very similar to that of the DS (Fig. 2, Fig. 3b), as also indicated by their similar CIA (around 75) and ICV (around 0.92) values (Fig. 3b, STable 1). However, the DS had SiO 2 /Al 2 O 3 ratios (6.34–16.9, average = 9.86) slightly higher than the FS (5.15–16.4, average = 7.87), and the SiO 2 /Al 2 O 3 ratios of the DS significantly decreased from the upper YP and FY sediments (6.3–16.9, average = 10.5) to the near dam SW and JG sediments (6.34–9.4, average = 7.5). While, those of the FS veried oppositely, with upper HG and JZ sediments (5.15–9.8, average = 6.78) being slightly lower than that of the lower river SG and XW sediments (5.15–16.4, average = 9.1 (STable 1). As SiO 2 /Al 2 O 3 ratio represents the content of detrital minerals (e.g., quartz) relative to clays (e.g., kaolinite) in sediments, it is commonly used to qualtify the sediment grain size variation (Bbek, et al., 2015 ; Greber and Dauphas, 2019 ). Thus, the distinct decrease of SiO 2 /Al 2 O 3 ratios from the upper river to near dam in the DS indicated significantly the decrease of hydrological dynamics for sediment deposition due to dam construction (Fig. 1b). While, the deposition dynamic setting for the pre-dam fluvial system varied oppositely probably due to the river bed slope. Therefore, the DS resulted from the deposition dynamics slightly different from that of the pre-dam FS. 3.1.2 Trace metals Trace metals including Ni, Mn, Cu, Zn, Pb, Cd, Co, V, Cr, Th, Zr, and Hf in the DS had distinctly variable concentrations (Cv > 0.2), while those of the rest (Ba, Sc, U, Tl, Cs, Ga, Ge, Rb, Sr, Nb, Ta, and REE) were less variable (Cv < 0.2). It is seen that heavy metals Mn, Ni, Cu, Zn, Pb, and Cd in the DS had concentrations (STable 1) significantly higher than the XJR (Zhao et al., 2021 ) as shown in Fig. 4, in which Ni and Cd had concentrations ranging from 23.67 to 7699 (mg/kg) and 0.28 to 25.9 (mg/kg), with averages of 494 and 3.32 (mg/kg), respectively (Fig. 4a, b). Also, sample CS1 had extremely higher concentrations of Cd (25.9 mg/kg), Pb (162.2 mg/kg), and Zn (1080.8 mg/kg) than other samples (Fig. 4d). However, other trace metals (except Zr and Hf) in the DS generally had concentrations comparable to that of the XJR (Zhao et al., 2021 ). The EF values showed that heavy metals (Mn, Ni, Cu, Zn, Pb, and Cd) were significantly enriched (with EF > 1.5), while otheFS (except Zr and Hf) were neither enriched nor depleted (EF around 1.0) in the DS (Fig. 5a). The different distribution patterns between heavy metals (Mn, Ni, Cu, Zn, Pb, and Cd) and other trace metals in the DS may suggest that these heavy metals had sources different from other trace metals (e. g., Darwish et al., 2018; Dueñas-Moreno et al., 2024 ). It is interesting to find that the distribution of trace metals (except Ni) in the FS was very similar to that in DS, as shown by metal concentrations (Fig. 4a-c) and the EF values (Fig. 5a, b). Concentrations of some terrigenous pair metals such Zr-Hf, Nb-Ta, Ga-Ge, Rb-Sr, Rb-Cs, and Nd-Sm in the FS and DS were positively linear correlated (R 2 > 0.50) to each other, with ratios such as Zr/Hf, Nb/Ta, Ga/Ge, Rb/Sr, Rb/Cs, and Nd/Sm of the FS equal to that of the DS (STable 1). For example, Zr/Hf ratios of the FS ranged from 32.3 to 36.9 with an average of 34.7, being equal to those (33.5 to 36.8, average = 34.8) of the DS. The similar ratio values of these pair metals in the DS and FS not only informed that these pair metals behaved similarly during weathering, transporting, and deposition processes, but also indicated that the DS and FS resulted from the similar terrigenous sources (Singh, 2009 ), because these terrigenous trace metals are generally hosted in deterital minerals, such as Zr in zircon, Rb, and Cs in clay (Fang et al., 2021 ). Therefore, the natural processes in the water for the DS deposition after 2014 worked the same to the FS deposition before 2011, and they had impacted less on the differences of trace metal distribution in the DS and FS. More importantly, the DS had REE distribution patterns very similar to the FS. Concentrations of total REE (∑REE) in the DS ranged from 182.5 to 343.4 (mg/kg) with an average of 275.9 mg/kg (n = 52), being comparable to that of the FS that had ∑REE ranging from 148.1 to 531.1 (mg/kg) with an average of 295.2 mg/kg (n = 95) (STable 1). Also, the DS displayed two types of REE distribution patterns, the V-shape and flat–shale types (Fig. 6). Both REE types had (La/Yb) N , (La/Sm) N , (Gd/Yb) N , and Ce/Ce * ratios around 1.28, 1.13, 1.11, and 0.92 respectively, with Eu/Eu * values around − 0.16 for the V-shape and 0.07 for the flat-shale type (Fig. 6a, STable 1). Such a REE distribution pattern was very similar to that of the FS (Fang et al., 2023 ). This provides a further support to the above conclusion, and natural processes taking place in the water had resulted in a limited variation on trace metal distribution in the DS and FS. Moreover, ratio of terrigenous pair metals such as Zr/Hf, Nb/Ta, Ga/Ge, Rb/Sr, Rb/Cs, and Nd/Sm in the DS and FS were comparable to that of the UCC (Gao et al., 1999 ) and XJR (Zhao et al., 2021 ) (STable 1). This indicates that the terrigenous compositions of the DS and FS were resulted from averaging of the upper river source rocks (sedimentary rocks and granites) through weathering, hydrological sorting, and mixing (Sharma et al., 2013 ; Wu et al, 2021 ). 3.1.3 Difference of metal distribution between the post- and pre-dam sediments Although the DS and FS were characterized by similar major (Figs. 2, 3) and trace (Fig. 5, 6) element compisitions, the normality via the Shapiro-Wilk method (Perkins and Mason, 1995) suggested that trace metals determined to be non-normally distributed in at least one sample population included Ni, Mn, Zn, Cd, Cu, and Pb (Fig. 4a-c). Significant differences (α = 0.05) exist for these six metals with the higher mean and median concentrations for all elements in both the DS and FS. The difference with respect to Ni was the result of the singularly high concentrations ranging from 23.67 to 7699.5 (mg/kg) with an average of 449.5 mg/kg (n = 52) in the DS, being distinctly higher than that of the FS (from 24.75 mg/kg to 93.45 mg/kg with an average of 48.7 mg/kg, n = 95) (Fig. 4a). Statistical results suggested that Ni concentration in the DS had an increase of 915% (p = 0.0004) relative to the FS (Fig. 4a), and there were 69.2% of DS samples containing Ni concentrations higher than its LBV (48.5 mg/kg) of the XJR (Zhao et al., 2021 ). Ni was scattered in the DS with the higher concentrations in upper river YP and down river JG sediments (Fig. 4d), and no grain size effect on Ni concentration was found (Ni concentrations in the DS were less significantly correlated to SiO 2 /Al 2 O 3 ratios, see below). While, such a high Ni concentration in the DS had rarely been observed in the past not only in this river (e. g., Peng et al., 2011 ; Fang et al., 2019 ), but also in many otheFS around the world. For our knowledge, only the high concentration of Ni (330 mg/kg) in sediments was found at the Panzihua city of the Yangetze river in China (Wu et al., 2013 ), where Ni-bearing ores were exploited (Wu et al., 2013 ). While, Ni concentrations in many dam sediments were low, for example, sediments from the Kafrain dam had Ni concentration in average of 170 mg/kg (Zhao., 2024), Kapulukaya Reservoir 65.8 mg/kg (Kankılıç et al., 2013 ), Iron Gate Reservoir 74.5 mg/kg (Zhao et al., 2024 ), Antweiler and Vaussaire Reservoir 58.3 (mg/kg) (Frémion et al., 2016 ), being much lower than those in the DS of this study. Thus, it is particularly essential to strengthen such an extremely high Ni concentration in the DS. The difference with respect to Mn, Zn, Cd, Cu, and Pb was the result of a relatively lower concentrations in the DS (except in sample SC1, STable 1). Statistical results suggested that concentrations of these metals in the DS had a decrease of about 23% (p = 0.0001), 49% (p < 0.0001), 76% (p = 0.00003), 34% (p = 0.00001), and 49% (p < 0.0001) respectively, relative to that in the FS (Fig. 4a-c). This is quite different from other observations which found that concentrations of trace metals in dam sediments were increased relative to the fluvial sediments (e.g., Bing et al., 2016 ). As the natural processes in the wateFShed affected less on trace metal distribution in the DS and FS, this concentration decrease was probably resulted from the remove of industrial plants such as the ZMP, STC, and ZCD (Fig. 1b) in lower reaches, where the non-ferrous minerals and ores were once smelted and refined therein (Peng et al., 2011 ; Fang et al., 2019 ). Therefore, the difference of trace metal distribution between the DS and FS was characterized by heavy metals Ni, Mn, Zn, Cd, Cu, and Pb Many studies have concluded that the accumulation and contamination of heavy metals in dam sediments were regulated by the spatial variation in sediment particle size, OM and Fe/Mn oxide contents (Audry et al., 2010 ; Frémion et al., 2016 ), and other chemical components (e.g., Guo et al., 2023 ; Cieśla and Gruca-Rokosz, 2024 ). However, this study on compaFSion of geochemical composition and pair metal ratio of the DS and FS suggests that heavy metal distribution an contamination in the DS were resulted from anthropogenic inputs of these metals, which were less associated with the spatial variation in particle size (SiO 2 /Al 2 O 3 ratio), OM (content of LOI) and Fe/Mn oxide contents in the DS. Thus, the distribution of heavy metal contamination in dam sediments was mainly resulted from anthropogenic activities in the water. 3.2 Source discrimination of trace metals in dam sediments 3.2.1 Dam impacting on the heavy metals anthropogenic sources input PCA and Pearson's correlation analysis are frequently applied to discriminate the trace metals of natural sources from those of anthropogenic inputs for understanding and assessing the heavy metal contamination in sediments (e.g., Fang et al., 2019 ). Major elements in sediments represent the definite mineralogical compositions of the sediments, such as SiO 2 for silicate minerals (e.g., quartz, feldsapr, etc.), and Al 2 O 3 the clay (e.g., Sharma et al., 2013 ; Fang et al., 2021 ). Thus, the association of trace metal with major elements is applied to determine at least the host mineral phases of trace metals in sediments and then to disctiminate their sources (Fok et al., 2013 ; Wu et al., 2021 ). Here, PCA was performed based on the Al-normalized concentrations of major and trace elements in the DS samples (n = 52). The Kaiser–Meyer–Olkin (KMO) value for the PCA was 0.718 (> 0.6), and the associated probability of the Bartlett sphericity test was 0. That indicates that PCA could be applied to the dimensionality decompositions (Sreelesh et al., 2025). The PCA results revealed that the variability of major and trace elements could be expressed as three principal components (PC1, PC2, and PC3) that explained 74.052% of the total variance with relative contributions of 51.018%, 12.331%, and 10.724% respectively. These data were plotted in a PC1 vs. PC2 vs. PC3, plot (Fig. 7) to visualize the two groups of elements. Group A that had loadings from − 0.6 to 0.8 for PC1, PC2, and PC3 included heavy metals Mn, Zn, Cd, Cu, Pb, Ni, Co, V, Cr, and Sc, and major elements Fe 2 O 3 , MnO, P 2 O 5 , and LOI. Group B that had loadings from − 0.2 to 0.8 for PC1, -0.7 to -0.4 for PC2, and − 0.4 to 0.4 for PC3 included the rest of trace metals, and major elements SiO 2 , TiO 2 , CaO, MgO, K 2 O, and Na 2 O (Fig. 7). This grouping scheme matches the concentration variation and enrichment (EF values) features of major and trace elements in the DS (Fig. 5a, b, STable 1). The Pearson's correlation analysis (STable 1) suggested that metals Ni, Mn, Zn, Cd, Cu, and Pb were significantly postively correlated to each other (r > 0.52, p = 0.001), and they (except Cd, and Ni) were also positively correlated to Al 2 O 3 (r > 0.55, p = 0.001), Fe 2 O 3 (r > 0.43, p = 0.001), MnO (r > 0.59, p = 0.001), and LOI (r > 0.34, p = 0.001). As metal Cd was significantly positively correlated to Zn (r = 0.99, p = 0.001) and Pb (r = 0.81, p = 0.001), and Ni correlated to Cu (r = 0.31, p = 0.001) (STable 1), it is suggested that these heavy metals were hosted in clays, Fe/Mn oxide minerals, and OM in the DS. Thus, heavy metals that had higher concentrations than other metals (Fig. 4, STable 1) and were significantly enriched (Fig. 5) in the DS were resulted from additional contribution, the anthropogenic activities (Zhao et al., 2024 ). While, other trace metals Co, V, Cr and Sc in Group A, and those in Group B (Fig. 7) that were correlated to silicate components SiO 2 , TiO 2 , MgO, K 2 O, and Na 2 O and had concentrations comparable to the LBV of the XJR (Zhao et al., 2021 ) with EF values around 1.0 (Fig. 5a) were mostly of the natural sources (Dhivert et al., 2015 ; Wu et al, 2021 ). The sources of heavy metals in fluvial sediments of the river have been documented by many studies, for which the anthorpogenic contribution of heavy metals has been attributed to the mining activities (e.g., smelting, refining, etc.) popuparized in the watershed (e.g., Peng et al., 2011 ; Sun et al., 2012 ; Peng et al., 2022a , b ). Although the implementation of the environmental protection and ecological conservation project (Chai et al., 2016 ) has led to reduce the metal sources by removing industrial plants such as the ZMP, XSC, and ZCD in lower reaches (Fig. 1b), the mining activities has less been terminated in upper river areas due to the need of economic development. Thus, mining activities in upper river areas represent the major sources of heavy metals as before (Fang et al., 2019 ). The significant concentration decrease of the mine induced metals in the DS (Fig. 4a-c) indicates that (1) removing the major industrial plants in the lower reaches has taken effect on environmental protection and ecological conservation for the watershed; and (2) Ni with high concentrations in the DS was not possibly contributed from mining activities in upper river areas. Moreover, ratios of Ni/Co, Ni/Cr, and Cr/Co in the FS were definitely around 2.46, 0.52, and 4.77 respectively (STable 1), which were comparable to that of the UCC (2.24, 0.48, and 4.7 respectively) (Gao et al., 1999 ) and XJR (2.39, 0.53, and 4.5 respectively) (Zhao et al., 2021 ), illustrating that Ni as well as Cr and Co in the FS was contributed from source rocks of the watershed (Fang et al., 2021 ; Wu et al., 2021 ). However, ratios of Ni/Co and Ni/Cr in the DS ranged uncertainly from 1.4 to 324 and 0.36 to 15.6 respectively, being quite different from that in the FS. While, the Cr/Co ratios in the DS (except sample YP9 and YP19) were defintiely around 4.77, being comparable to that of the FS, UCC (Gao et al., 1999 ) and XJR (Zhao et al., 2021 ). That again suggests that Ni in the DS had source contribution different from metals Co and Cr in the DS and Ni in the FS, and metals Co and Cr in the DS were similarly and naturally contributed from source rocks as those were in the FS (Singh, 2009 ; Peng et al., 2011 ). The higher Ni concentrations (up to 7699.5 mg/kg) in the DS were observed in the YP sediments at Yinpengling district where the Chinese catering was concentrated along the dam river since 2014 and in the JG sediments at the Jinggang village where distributed the shipping ports. Samples (n = 22) between the YP and JG sediments except sample SW6 and YP6 that Ni concentrations of 737.9 and 145.3 (mg/kg) respectively, generally had the Ni concentrations around 46 mg/kg (Fig. 1b, 4d). As metal Ni is less mobile in surface system (Liu et al., 2025 ), and it is usually used for ceramic colour in catering and corrosion protection coating for shipes (Peluso et al., 2013 ; Guo et al., 2023 ), the spatial distribution of Ni in the DS (Fig. 4d) implies that the waste discharges from catering and shipping activities in the dam reservoir may represent the major sources of Ni in the DS. Therefore, pathways for heavy metals in the DS included those from natural process (e.g., rock weathering), mining activities in upper river areas, and catering and shipping activities within the dam reservoir. The natural processes led to the fundmental distribution of heavy metals in the DS, and the anthropogenic activities in upper river areas and within the dam led the changes of trace metal distribution patterns (Fig. 4, and 5a, b) and caused the heavy metal contamination (Fig. 9). 3.2.2 Analysis of pollution sources based on APCS-MLR model We initially assessed the contribution of various pollution sources to the heavy metals present in the sediments and computed the absolute principal component scores for each sample. The overall variance was 0.048. The results of this analysis were reliable, as evidenced by the error rate between the measured and predicted concentrations of most elements being less than 1%(Peng et al., 2024). Finally, using Eq. (5), the influence of each pollution factor on the sediment's heavy metal content was assessed, as shown in Fig. 8. Natural sources accounted for 40.7% of total contribution in DS sediment, and was loaded with Ba (67.96%) ,Sc (70.94%),V (93.14%), Co (67.93%), Th (61.39%) and U (79.86%). Mn, Zn, Pb, Cd were loaded in industry sources, with the loading as following in order 43.06%, 61.52%, 44.88%, 86.92%. Agricultural and catering sewage sources had 17.5% accounts for the sediment total contribution, and heavy metals Cr, Ni, Cu had high loading. Tl was affected by mixed sources of 46.92%. The construction of dams appeared to affect the loading rate of certain heavy metal pollution sources. For example, natural sources had the highest contribution for heavy metal Ni in FS sediment, and metals Mn, Zn, Pb etc. were higher loaded on industry sources in FS sediment (Fang et al., 2019 ). Meanwhile, after dam construction, agricultural and domestic sewage sources and mixed sources contribution in sediment seems to be increased; but industry sources contribution in sediment decreased from 42.31–31.1% (Fang et al., 2019 ). Dam construction may accelerate the massive accumulation of agricultural and catering pollutants (Fremion et al., 2016; Guo et al., 2023 ; Zhao et al., 2024 ), which lead to the changes of construction. The changes of industry sources contribution in sediment may mainly relate to pollution prevention and control projects by government (Gao et al., 2022a ). The outcomes of the APCS-MLR model aligned with the findings from the related PCA analysis (Fig. 7). 3.3 Contamination of heavy metals in dam sediments The I Geo values of heavy metals calculated using Eqs. (2) and (3) were summarized in STable 1 and displayed in Fig. 5c, and they showed that Cu, Zn, and Pb represented less significant contamination in the DS (I Geo < 1.5), with only 13.5% samples being slightly and moderately contaminated in Zn (1.5 < I Geo < 2.5). Thus, contamination of Cu, Zn, and Pb in the DS was insignificant althrough sample SC1 had high concentrations of Zn and Pb (Fig. 4d). Mn and Cd represented moderate level of contamination (1.5 < I Geo 4.0). It is seen that contamination level of metals Cu, Zn, Pb, Mn, and Cd in the DS was significantly lower than that of the FS (STable 1). While, Ni represented a moderate to high level of contamination, for which about 44.2% samples was highly contaminated (1.5 < I Geo 4.0). Thus, the contamination of Ni in the DS was very serious. 3.4 Assessment of Human Health Risk Models To assessment the potential health risks of heavy metals in dam sediment, both dermal and ingestion contact had been used as a primary exposure pathways to child and adult. The findings summarized in Table S1 , indicating that except for Ni and Cd in some samples(HI > 1), the HI values of other heavy metals (Mn, Cu, Zn, Pb)for adults and child are within an acceptable range (HI < 1). The non carcinogenic risk points of Ni for adult and child are mainly in YP9 (HI = 3.2389/4.9751), YP18 (HI = 1.6871/2.5346), and YP19 (HI = 2.5579/3.0654). The non carcinogenic risk of Cd to adult and child is mainly located in SC1 (HI = 1.2579/2.4587). Based on the above analysis, it can be concluded that the overall non carcinogenic risk of heavy metals in the sediments of the Xiangjiang Reservoir area is low. However, due to the non carcinogenic risk of Ni and Cd to humans, especially child, in some sediments, it is necessary to strengthen the control of these two elements in the process of pollution prevention and control in the reservoir area. 3.5 Formation processes of heavy metal contamination in dam sediments The source disticmination by PCA, Pearson's correlation analysis and APCS-MLR model in this study suggested that heavy metals in the DS were contributed from natural processes and anthropogenic activities, and those from anthropogenic activities such as mining activities in upper river areas, and catering and shipping activities within the dam reservoir led to the heavy metal contamination and then impacted to the ecological system of the waters. Contamination of Cd, Mn, Cu, Pb and Zn in DS resulted from inputs of heavy metals from mining activities in upper river areas, while this of the Ni in the DS was associated with waste discharges from catering and shipping activities within the reservoir(Fleischmann et al., 2025 ). Thus, the distribution and contamination of heavy metals in the DS can be summarized in Fig. 9, and attentions should be paid further to manage the wastes discharges from mining activities in upper river areas and from catering and shipping activities within the reservoir . 4. Conclusions (1) The dam sediments deposited after 2014 had the major and trace element compositions similar to that of the pre-dam fluvial sediments deposited on site before 2011. Major elements Fe 2 O 3 , MnO, P 2 O 5 and LOI, and trace metals Mn, Zn, Cd, Cu, Pb, and Ni had variable concentrations and were enriched in the sediments. While, major elements SiO 2 , TiO 2 , CaO, MgO, K 2 O and Na 2 O, and other terrigenous trace metals (Ba, Sc, Tl, Cs, Ga, Ge, Rb, Sr, Nb, Ta, Zr, Hf, and REE) in the sediments had concentrations less variable and comparable to the source rocks. (2) Differences of trace metal distribution in dam sediments was characterized by a distinct increase of Ni concentration and decreases of concentrations of Mn, Zn, Cd, Cu and Pb relative to the pre-dam fluvial sediments. The dam sediments were contaminated in these metals, with contamination for Ni and Cd in high degree and others in slight degree. The contamination of Cd and Ni in the dam sediments presented a high level of potential ecological risk for the reservoir waters. (3) It is worth noting that metals Ni and Cd have non carcinogenic risk for adult and child in some samples of dam sediment. As heavy metals from wastes like mining activities in upper river areas may led to the contamination of Mn, Zn, Cd, Cu, and Pb in the sediments, but catering and shipping activities within the reservoir may be the main cause of the Ni contamination in sediments. So protection for heavy metals (especially Ni, Cd) contamination in the dam reservoir should pay a great attention to the anthropogenic activities both in upper river areas and within the dam reservoir. Declarations Acknowledgement This study was financially supported by the Construction Program for Fish-Class Disciplines (Geography-5010002) of Hunan Province (China), and the Key Project of Development Biology & Breeding (2022XKQ0207) from Hunan Province (China). Part of the work was also supported by the Scientific Research Funds of Hunan Provincial Education Department (Grant No. 18A012) and grants (Grant No. 2022JJ30030) from Department of Science and Technology of Hunan province (China). Associate Prof. Xianglin Tu at the Guangzhou Institute of Geochemistry, Chinese Academy of Science is thanked for helping for analyses. Authors contribution Bo peng contributed to the Writing—original draft, conceptualisation, data curation. Xia yang formal analysis, Methodology, and Investigation. Si cheng Wu contributed to the visualisation and Software. Nengqiu Wu, Hongjie Hu and Xianjia Du contributed to reviewing and supervision. Yunhan Qu, Yanan Dai and Xin Wang contributed to reviewing. Funding This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability Data will be made available on request. Conflict of interest The authors declare no competing interests. References Almeida, T. V. P., Sales, C. F., Ribeiro, Y. M., Sobjak, T. M., Bazzoli, N., Melo, R. M. C., Elizete Rizzo, E. (2024). Metal-contaminated sediment toxicity in a highly impacted Neotropical river: Insights from zebraffsh embryo toxicity assays. Chemosphere 362, 142627. https://doi.org/10.1016/j.chemosphere.2024.142627. Audry, S., Grosbois, C., Bril, H., Schäfer, J., Kierczak, J., Blanc, G. (2010). Post-depositional redistribution of trace metals in reservoir sediments of a mining/smelting-impacted wateFShed (the Lot River, SW France). Appl. Gepchem. 25, 778-794. https: // doi: 10. 1016/j. envpol. 2004. 05. 025. Bing, H., Zhou, J., Wu, Y., Wang, X., Sun, H., Li, R. (2016). Current state, sources, and potential risk of heavy metals in sediments of Three Gorges Reservoir, China. Environ. Pollut. 214, 485-496. http: //dx. doi. org/10. 1016/j. envpol. 2016. 04. 062. Bbek, O., Grygar, T M., Faměra, M. (2015). Geochemical background in polluted river sediments: How to separate the effects of sediment provenance and grain size with statistical rigour? Catena, 135: 240 - 253. https:// doi: 10. 1016 /j. catena. 2015. 07.003. Chai, L., Li, H., Yang, Z., Min, X., Liao, Q., Liu, Y., Men, S., Yan, Y., Xu, J. (2016). Heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan, China: distribution, contamination, and ecological risk assessment. Environ. Sci. Pollut. Res. 24, 874-885.https: //doi. org/10.1007/s11356–016–7872–x. Chen, D., Peng, B., Feng, X., Wu, S., Liu, J., Zhao, Y., Dai, Y. (2021). Geochemistry of major elements in bed sediments from inlets of the Four RiveFS to Dongting Lake, China. Quaternary Sci. 41(5), 1267-1280 (in Chinese with an English abstract). https: //doi. org/10.11928/j.issn.1001-7410.2021.05.04. Cieśla, M., Gruca-Rokosz, R. (2024). Fate of heavy metals in ecosystems of dam reservoirs: Transport, distribution and signiffcance of the origin of organic matter. Environ. Pollut. 361, 124811. https://doi.org/10.1016/j.envpol.2024.124811. Darwish, M. A. G., Pöllmann, H. (2018). Secondary dispersion of trace elements in bottom sediments of the High Dam Lake, South Egypt and North Sudan. Arabian Journal of Geosciences 11, 773. https://link.springer.com/article/10.1007/s12517-018-4127-9. Dhivert, E., Grosbois, C., Rodrigues, S., Desmet, M. (2015). Influence of fluvial environments on sediment archiving processes and temporal pollutant dynamics (Upper Loire River, France). Sci. Total Environ. 505, 121-136. https://doi.org/10.1016/j.scitotenv.2014.09.082. Dueñas-Moreno, J., Mora, A., Narvaez-Montoya, C., Mahlknecht, J. (2024). Trace elements and heavy metal(loid)s triggering ecological risks in a heavily polluted river-reservoir system of central Mexico: Probabilistic approaches. Environ. Res. 262, 119937. https://doi.org/10.1016/j.envres.2024.119937. Fang, X., Peng, B., Wang, X., Song, Z., Zhou, X ., Wang, Q., Qin, Z., Tan, C. (2019). Distribution, contamination and source identification of heavy metals in bed sediments from the lower reaches of the Xiangjiang River in Hunan Province, China. Sci. Total Environ. 689, 557-570. https: // doi.org /10. 1016 / j. scitotenv. 2019. 06. 330. Fang, X. H., Peng, B., Song, Z. L., Wu, S. C., Chen, D. T., Zhao, Y. F., Liu, J., Dai, Y. N., Tu, X. L. (2021). Geochemistry of heavy metal-contaminated sediments from the Four River inlets of Dongting lake, China. Environ. Sci. Pollut. Res. 28, 27593-27613. https: //doi. org/10. 1007/s11356–021–12635–0. Fang, X., Peng, B., Guo, X., Wu, S., Xie, S., Wu, J., Yang, X., Chen, H., Dai, Y. (2023). Distribution, source and contamination of rare earth elements in sediments from lower reaches of the Xiangjiang River, China. Environ. Pollut., 336, 122384. https: //doi. org/10. 1016/j. envpol. 2023. 122384. Fleischmann, S., Scholz, F., Du, J., Scholten, J., Vance, D. (2025). Processes controlling nickel and its isotopes in anoxic sediments of a seasonally hypoxic bay. Geochim. Cosmochim. Acta 391, 1-15. https://doi.org/10.1007/s10653-025-02658-8. Fok, L., Peart, M. R., Chen, J. (2013). The influence of geology and land use on the geochemical baselines of the East River basin, China. Catena 101, 212-225. https://doi.org/10.1016/j.catena.2012.09.008. Fonseca, R., Pinho, C., Oliveira, M. (2016). The influence of particles recycling on the geochemistry of sediments in a large tropical dam lake in the Amazonian region, Brazil. J. South American Earth Sci. 72, 328-350. https://doi.org/10.1016/j.jsames.2016.09.012. Frémion, F., Bordas, F., Mourier, B., Lenain, J. F., Kestens, T., Courtin-Nomade, A., 2016. Influence of dams on sediment continuity: A study case of a natural metallic contamination. Sci. Total Environ. 547, 282-294. http: //dx. doi. org/10. 1016/j. scitotenv. 2016. 01. 023. Gao, S., Lu, T., Zhang, B., Zhang, H., Han, W., Zhao, Z. (1999). Structure and chemical compositions of East China upper continental crust. Sci. China (series-D) 42(2), 129-140 (in Chinese with an English abstract). Gao, L., Gao, B., Xu, D., Peng, W., Lu, J. (2019). Multiple assessments of trace metals in sediments and their response to the water level fluctuation in the Three Gorges Reservoir, China. Sci. Total Environ. 648, 197-205. https: //doi. org/10. 1016/j. scitotenv. 2018. 08. 112. Gromet, L. P., Haskin, L. A., Korotev, R. L., Dymek, R. F. (1984). The “North American shale composite”: its compilation, major and trace element characteristics. Geochim. Cosmochim. Acta. 48, 2469-2482. https: //doi.10.1016/0016-7037(84)90298-9. Guo, J., Xie, Y., Guan, A., Qi, W., Cao, X., Peng, J., Liu, H., Wu, X., Li, C., Wang, D., Qu, J. (2023). Dam construction reshapes sedimentary pollutant distribution along the Yangtze river by regulating sediment composition. Environ. Pollut. 316, 120659. https: //doi. org/10. 1016/j. envpol. 2022. 120659. Gao, Z., Wang, C., Zhang, X., Liu, Y., Zhang, Y., Liu, K., Jia, Y. (2022a). Pollution and Sources of Heavy Metals in Sediments of Changsha Section of Xiangjiang River. Journal of Yantai University(Natural Science and Engineering Edition), 36(1): 120–126. https: // doi: 10. 13951/j. cnki. 37_1213/n. 211015. Greber, N D., Dauphas, N. (2019). The chemistry of fine-grained terrigenous sediments reveals a chemically evolved Paleoarchean emergedcrust. Geochimica et Cosmochimica Acta, 255: 247-264. https: //doi: 10.1016/j.gca.2019.04.012. Hu, F., Huang, H., Liu, Z., Hou, Y. (2020). Analysis and evaluation of heavy metal residues in fishes in the Changsha section of Xiangjiang River after impoundment of Xiangjiang Comprehensive Hub Project. Mining and Metallurgical Engineering 40(4), 114-119 (in Chinese with an English abstract). https: //doi:CNKI:SUN:KYGC.0.2020-04-030. Jafarabadi, A., Bakhtiyari, A R., Toosi A S. (2017). Spatial distribution, ecological and health risk assessment of heavy metals in marine surface sediments and coastal seawaters of fringing coral reefs of the Persian Gulf, Iran.Chemosphere,185:1090–1111. https://doi.org/10.1016/j.chemosphere.2017.07.110. Kankılıç, G. B., Tüzün, İ., Kadıoğlu, Y. K. (2013). Assessment of heavy metal levels in sediment samples of Kapulukaya Dam Lake (Kirikkale) and lower catchment area. Environ. Monit. Assess. 185, 6739-6750. https://link.springer.com/article/10.1007/s10661-013-3061-2. Li, X., Bing, J., Zhang, J., Guo, L., Deng, Z., Wang, D., Liu, L. (2022). Ecological risk assessment and sources identification of heavy metals in surface sediments of a river–reservoir system. Sci. Total Environ. 842, 156683. https://doi.org/10.1016/j.scitotenv.2022.156683. Lin, L., Li, C., Yang, W., Zhao, L., Liu, M., Li, Q., Crittenden, J. C. (2020). Spatial variations and periodic changes in heavy metals in surface water and sediments of the Three Gorges Reservoir, China. Chemosphere 240, 124837. https://doi.org/10.1016/j.chemosphere.2019.124837. Liu, S., Wu, K., Yao, L., Li, Y., Chen, R., Zhang, L., Wu, Z., Zhou, Q. (2024). Characteristics and correlation analysis of heavy metal distribution in China’s freshwater aquaculture pond sediments. Sci. Total Environ. 931, 172909. https://doi.org/10.1016/j.scitotenv.2024.172909. Liu, S., Yu, F., Lang, T., Ji, Y., Fu, Y., Zhang, J., Ge, C. (2023). Spatial distribution of heavy metal contaminants: The effects of water-sediment regulation in the Henan section of the Yellow River. Sci. Total Environ. 892, 164568. https://doi.org/10.1016/j.scitotenv.2023.164568. Lv, J. (2019). Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. Environ. Pollut. 244, 72–83. https://doi.org/10. 1016/j. envpol. 2018. 09. 147. Mclennan, S M., Hemming, S R., Mcdaniel, D K. (1993). Geochemical approaches to sedimentation, provenance, and tectonics. Geological Society of America Special Papers, 284: 21-40. https: //doi: 10. 1130 /SPE284-p21． Liu, X. Y., Wu, Y. F., D. Gregory, D. D., Evans, K., Williams-Jones A. E., Zhang, W., Zhou, M. F., Li, J. W. (2025). Remobilization of Ni in framboidal pyrite within sulfide nodules in Early Cambrian nickeliferous black shales. Chem. Geol. 691, 122913. https://doi.org/10.1016/ j.chemgeo.2025.122913. Nesbitt, H. W., Young, G. M. (1982). Early Proterozoic climates and plate motions inferred from major element chemistry of lutites. Nature 299, 715-717. https: //doi:10.1038/299715a0. Nguyen, T. M. Y., Vanreusel, A., Mevenkamp, L., Laforce, B., Lins, L., Thanh, T. T., Van, D. N., Xuan, Q. N. (2022). The effect of a dam on the copper accumulation in estuarine sediment and associated nematodes in a Mekong estuary. Environ. Monit. Assess. 194 (Suppl 2), 772. https://link.springer.com/article/10.1007/s10661-022-10183-9. Pacheco, F. A. L., Valle Junior, R. F. V., Silva, M. M. A. P., Pissarra, T. C. T., Rolim , G. de S., Melo, M. C., Valera, C. A., Moura, J., Fernandes, L. F. S. (2023). Geochemistry and contamination of sediments and water in rivers affected by the rupture of tailings dams (Brumadinho, Brazil). Appl. Geochem. 152, 105644. https://10.1016/j.apgeochem.2023.105644. Peluso, L., Bulus Rossini, G., Salibián, A., Ronco, A. (2013). Physicochemical and ecotoxicological based assessment of bottom sediments from the Luján River basin, Buenos Aires, Argentina. Environ. Monit. Assess. 185, 5993–6002. https://link.springer.com/article/10.1007/s10661-012-3000-7. Peng, B., Tang, X., Yu, C., Tan, C., Yin, C., Yang, G., Liu, Q., Yang, K., Tu, X. (2011). Geochemistry of trace metals and Pb isotopes of sediments from the lowermost Xiangjiang River, Hunan Province (P. R. China): implications on sources of trace metals. Environ. Earth Sci. 64 (5), 1455–1473. https: //doi. org/10. 3724/SP. J. 1011. 2011. 00181. Peng, B., Albert, J., Fang, X., Jiang, C., Wu, S., Li, X., Xie, S., Dai, Y. (2022a). Lead isotopic fingerprinting as a tracer to identify the sources of heavy metals in sediments from the Four Rivers’ inlets to Dongting Lake, China. Catena 219, 106594. https://doi.org/10.1016/j.catena.2022.106594. Peng, B., Chen, H. S., Fang, X. H., Xie, S. R., Wu, S. C., Jiang, C. X., Dai, Y. N. (2022b). Distribution of Pb isotopes in different chemical fractions in bed sediments from lower reaches of the Xiangjiang River, Hunan province of China. Sci. Total Environ. 829, 154394. https: //dx. doi. org/10. 1016/j. scitotenv. 2022. 154394. Peng, Y., Yu, G. (2024). Assessment of heavy metal pollution on agricultural land in Chengdu city under different anthropogenic pressures based on APCS – MLR modelling. Ecological Indicators, 165, 112183. https: //doi.org/10.1016/j.ecolind.2024.112183. Perkins, R. B., Mason, C. E. (2015). The relative mobility of trace elements from short-term weathering of a black shale. Appl. Geochem. 56, 67-79. https: //10.1016/j.apgeochem.2015.01.014. Roddaz, M., Viers, J., Brusset, S., Baby, P., Boucayrand, C., Hérail, G. (2006). Controls in weathering and provenance in the Amazonian foreland basin: insights from major and trace element geochemistry of Neogene Amazonian sediments. Chem. Geol. 226, 31-65. https: //10.1016/j.chemgeo.2005.08.010. Sang, C., Zheng, Y., Zhou, Q., Li, D., Liang, G., Gao, Y. (2019). Effects of water impoundment and water-level manipulation on the bioaccumulation pattern, trophic transfer and health risk of heavy metals in the food web of Three Gorges Reservoir (China). Chemosphere 232, 403-414. https://doi.org/10.1016/j.chemosphere.2019.04.216. Sreelesh , G. V., Asha Rani, K., Sreelash,·K. (2025). Seasonal dynamics, sources, and health risks of trace and heavy metals in the tropical critical zone of the Western Ghats, India. Environ Geochem Health,47:349. https://doi.org/10.1007/s10653-025-02658-8. Sharma, A., Sensarma, S., Kumar, K., Khanna, P. P., Saini, N. K. (2013). Mineralogy and geochemistry of the Mahi River sediments in tectonically active western India: implications for Deccan large igneous province source, weathering and mobility of elements in a semi-arid climate. Geochim. Cosmochim. Acta 104, 63-83. https: // doi: 10.1016/j. gca.2012.11.004. Singh, P. (2009). Geochemistry and provenance of stream sediments of the Ganga River and its major tributaries in the Himalayan region, India. Chem. Geol. 269, 220-236. https: /10.1016/j.chemgeo.2009.09.020. Sun, W., Sang, L., Jiang, B. (2012). Trace metals in sediments and aquatic plants from the Xiangjiang River, China. J. Soils Sediments 12, 1649-1657. https: //doi. org/10. 1007/s11368–012–0596–8. USEPA. (2014). Framework for human health risk assessment to inform decision making. US Environmental Protection Agency Washington, DC. Retrieved May 7, 2025, from https://www.epa.gov/sites/default/files/2014-12/documents/hhra-framework-final-2014.pd. Vergilio, C. S., Lacerda, D., Souza, T. S., de Oliveira, B. C. V., Fioresi, V. S., de Souza, V. V., Rodrigues, G. R., de Araujo Moreira Barbosa, M. K., Sartori, E., Rangel, T. P., de Almeida, D. Q. R., de Almeida, M. G., Thompson, F., Rezende, E. (2021). Immediate and long-term impacts of one of the woFSt mining tailing dam failure worldwide (Bento Rodrigues, Minas Gerais, Brazil). Sci. Total Environ. 756, 143697. https://doi.org/10.1016/j.scitotenv.2020.143697. Vukovic, D., Vukovic, Z., Stankovic, S. (2014). The impact of the Danube Iron Gate Dam on heavy metal storage and sediment flux within the reservoir. Catena 113, 18-23. https://doi.org/10.1016/j.catena.2013.07.012. Wang, H., Yuan, W., Zeng, Y., Liang, D., Deng, Y., Zhang, X., Li, Y. (2022). How does Three Gorges Dam regulate heavy metal footprints in the largest freshwater lake of China. Environ. Pollut. 292, 118313. https://doi.org/10.1016/j.envpol.2021.118313. Wu, S., Peng, B., Fang, X., Xie, S., Li, X., Jiang, C., Dai, Y. (2021). Distribution and assessment of cadmium contamination in sediments from the Four River inlets to Dongting Lake, China. Environ. Sci. Pollut. Res. 28: 66072-66085. https: //doi. org/10. 1007/s11356–021–15636–1. Wu, W., Zheng, H., Xu, S., Yang, J., Liu, W. (2013). Trace element geochemistry of riverbed and suspended sediments in the upper Yangtze River. J. Geochem. Explor. 124, 67-78. https://doi.org/10.1016/j.gexplo.2012.08.005. Xu, D., Gao, B., Peng, W., Gao, L., Li, Y. (2019). Geochemical and health risk assessments of antimony (Sb) in sediments of the Three Gorges Reservoir in China. Sci. Total Environ. 660, 1433-1440. https://doi.org/10.1016/j.scitotenv.2019.01.014. Yan, M., Gu, T., Chi, Q., Wang, C. (1997). Abundances of elements of China soils and surface geochemical properties of elements. Geophys. Geochem. Explor. 21(3), 16-167 (in Chinese with an English abstract). https://CNKI:SUN:WTYH.0.1997-06-008. Yang, H. F., Yang, S. L., Xu, K. H., Millimand , J. D., Wang, H., Yang, Z., Chen, Z., Zhang, C. Y. (2018). Human impacts on sediment in the Yangtze River: A review and new perspectives. Global and Planetary Change 162, 8-17. https://doi.org/10.1016/j.gloplacha.2018.01.001. Zhao, Q., Ding, S., Lu,X., Liang, G., Hong, Z., Lu, M., Jing, Y. (2022). Water-sediment regulation scheme of the Xiaolangdi Dam influences redistribution and accumulation of heavy metals in sediments in the middle and lower reaches of the Yellow River. Catena 210, 105880. https://doi.org/10.1016/j.catena.2021.105880. Zhao, Y., Peng, B., Fang, X., Wu S., Jing, L., Chen, D., Dai, Y. (2021). Geochemical background of elements in bed sediments from lower reaches of the Xiangjiang River in Hunan Province, China. Geo. Rev. 67(2), 504-522 (in Chinese with an English abstract). https: //doi. 10.16509 /j.Georeview.2021.02.018. Zhu, H., Bing, H., Wu, Y., Zhou, J., Sun, H., Wang, J., Wang, X. (2019) . 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. https: //doi. org/10.1016/j. scitotenv. 2019. 02. 016. Zhuang, Q., Li, G., Liu, Z. (2018). Distribution, source and pollution level of heavy metals in river sediments from South China. Catena 170, 386-396. https: //doi. org/10. 1016/j. catena. 2018 06. 037. Zhao, Z., Li, S., Li, Y. ( 2024). Controlling factors and sources–specific ecological risks associated with toxic metals in core sediments from cascade reservoirs in Southwest China. Sci. Total Environ. 924, 171570. https: //doi. org/10. 1016/j. scitotenv. 2024. 171570. Zhao, Q., Ding, S., Ji, X., Hong, Z., Lu, M., Wang, P. (2021). Relative contribution of the Xiaolangdi Dam to runoff changes in the lower Yellow River. Land 10 (5), 521. https://doi.org/10.3390/land10050521. Additional Declarations No competing interests reported. Supplementary Files Stable1.xlsx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2025 Read the published version in Environmental Geochemistry and Health → Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 26 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 10 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 05 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7302369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499046676,"identity":"78f11f81-508c-49d1-9ec9-06070a32dfba","order_by":0,"name":"Xia Yang","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Yang","suffix":""},{"id":499046678,"identity":"2187ad9e-57a4-41ba-9295-f7defcb06f90","order_by":1,"name":"Bo Peng","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Peng","suffix":""},{"id":499046680,"identity":"64b9e493-6475-4db8-ab4c-a061049cc516","order_by":2,"name":"Sicheng Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYJCCA4wNQJK9B8YlWgvPGRK0MIC1SOQQqcXgRvrDAz932OTJR749JvHjD4Mc340Exs8FeLXkGBzsPZNWbHg7L02yt43BWPJGArP0DDxazG7kMBxmbDucuHF2jpkEbwND4oYbCWzMPHi1pD8AavmfuHHmGTPJP38Y6onQkmAA1HIgcb4Ej5k0DxtDggEhLfZn3gD90pacuIEnx9hatk3CcOaZh83S+LRItqc//vCzzS5xfvsZw5tv/tjI8x1PPvgZnxY4MDgApiQYoNFEBJAnUt0oGAWjYBSMQAAAX99Sv1gZ4DQAAAAASUVORK5CYII=","orcid":"","institution":"Hunan University of Arts and Science","correspondingAuthor":true,"prefix":"","firstName":"Sicheng","middleName":"","lastName":"Wu","suffix":""},{"id":499046681,"identity":"7be0b533-063e-4987-8d1d-96447d72569f","order_by":3,"name":"Nengqiu Wu","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Nengqiu","middleName":"","lastName":"Wu","suffix":""},{"id":499046682,"identity":"e85caa11-a7a1-4050-8703-7f34d94d3c8c","order_by":4,"name":"Hongjie Hu","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"Hu","suffix":""},{"id":499046683,"identity":"2ee6e855-3603-49c2-ab93-81b553cb31a6","order_by":5,"name":"Xianjia Du","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xianjia","middleName":"","lastName":"Du","suffix":""},{"id":499046684,"identity":"faadd7f0-33e9-4926-972e-4357caa333b4","order_by":6,"name":"Yunhan Qu","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yunhan","middleName":"","lastName":"Qu","suffix":""},{"id":499046685,"identity":"d78ba48c-d0db-40ed-b998-cdee1b2c1ded","order_by":7,"name":"Yanan Dai","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Dai","suffix":""},{"id":499046686,"identity":"78cbadc0-3db8-484e-8c31-d2281d9bfd19","order_by":8,"name":"Xin Wang","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-05 15:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7302369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7302369/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10653-025-02905-y","type":"published","date":"2025-11-26T15:58:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89259527,"identity":"c7a7a63a-ab85-4011-8746-f85751db8da2","added_by":"auto","created_at":"2025-08-18 06:28:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1937888,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the general geology (Fang et al., 2019) of the Xiangjiang wateFShed (a), a sketch showing locations of the dam at Changsha city (Hu et al., 2020) and the sampling sites, for which JG, WC, FY, and YP represent the four sampling sites (see text) in the dam (b), and a sketch showing the vertical profile of relative locations for sampling in the DS (2022) and FS (2011) for this study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/76e0402b002f7b1207aaa1b1.jpg"},{"id":89260360,"identity":"29831afa-9af8-4f24-a4e1-7fbc5a9576f2","added_by":"auto","created_at":"2025-08-18 06:44:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":749312,"visible":true,"origin":"","legend":"\u003cp\u003eBinary plots of SiO\u003csub\u003e2\u003c/sub\u003e% against oncentrations of major elements in the sediments from the dam reservoir (DS, post-dam after 2014) and river bed (FS, pre-dam before 2014). Data of the CAS (Yan et al., 1997), XJR (Zhao et al. 2021), YZ (Yan et al. 1997), UCC (Gao et al. 1999), NASC (Gromet et al., 1984), and HGR (Yan et al., 1997) are included for compaFSion.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/4cc22356b7d7e9e01b369ce2.jpg"},{"id":89259529,"identity":"205f7c82-bda8-4e99-8383-9b9b07119bb5","added_by":"auto","created_at":"2025-08-18 06:28:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233535,"visible":true,"origin":"","legend":"\u003cp\u003eA-CN-K diagram (a) showing the degree of chemical weathering taking place in source rocks of the water. A = Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, CN = CaO* + Na\u003csub\u003e2\u003c/sub\u003eO, K = K\u003csub\u003e2\u003c/sub\u003eO, all are in molecular ratios; CaO\u003csup\u003e*\u003c/sup\u003e represents the CaO in silicates and is corrected after Roddaz et al. (2006), and Plot of Log(SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) vs. Log(Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e/K\u003csub\u003e2\u003c/sub\u003eO) (b) showing the chemical maturity of the sediments (Fang et al., 2021) (Legend is the same as in Fig. 2).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/408fc08d86ed5eda59600b74.jpg"},{"id":89259862,"identity":"caecfa84-cf8e-4705-b105-ccb0e820c82e","added_by":"auto","created_at":"2025-08-18 06:36:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":743498,"visible":true,"origin":"","legend":"\u003cp\u003eBox-plots of concentrations of heavy metals Cd, Cd (a), Cu, Pb (b), Ni, Mn, and Zn (c) in the DS and FS sediments, showing significant variations of heavy metal concentrations between the DS and FS; and plots of concentrations of Ni, Pb, Zn and Cd vs. dam distance (d) showing spatial distribution of these metals in the DS.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/1572fd462b0ec8a73c3ac6b7.jpg"},{"id":89259866,"identity":"39932f83-a8a7-4b30-a8b1-dc32137a34ff","added_by":"auto","created_at":"2025-08-18 06:36:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":357886,"visible":true,"origin":"","legend":"\u003cp\u003eBox-plots of EF values of trace metals in the DS (a) and FS (b), showing significant enrichment of metals Mn, Ni (except in the FS), Cu, Zn, Pb and Cd in the sediments, with less enrichment of other, and I\u003csub\u003eGeo\u003c/sub\u003e values of heavy metals in the DS (c), showing contamination levels of them.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/61ee48cdd518ed064aca677d.jpg"},{"id":89259539,"identity":"9f8ee125-486d-408f-8974-58fd91031d34","added_by":"auto","created_at":"2025-08-18 06:28:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":428430,"visible":true,"origin":"","legend":"\u003cp\u003eNASC normalized REE patterns of dam sediments, showing two types of REE patterns: flat shale like type with Eu/Eu* value around 0.07, and V-shape type with Eu/Eu* value around - 0.16.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/c4236a51ccf9b434d85666b3.jpg"},{"id":89259542,"identity":"de6a7b18-bc47-4167-9d01-944b66f261b0","added_by":"auto","created_at":"2025-08-18 06:28:03","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":222006,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal components PC1 vs. PC2 vs. PC3 plots showing two element groups and different associations of trace metals with major elements in the DS.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/a042eb1fe7b1540620db2296.jpg"},{"id":89260362,"identity":"d531befb-51e8-45f6-a1f7-10fc1f23b344","added_by":"auto","created_at":"2025-08-18 06:44:03","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":336768,"visible":true,"origin":"","legend":"\u003cp\u003eApportionment of heavy metal sources in Xiangjiang River sediments using the APCS-MLR model.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/b43095926dbc4675b5bf33c6.jpg"},{"id":89259882,"identity":"247f6c36-d8b0-4df2-9f5a-e393f49bb26e","added_by":"auto","created_at":"2025-08-18 06:36:04","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":209535,"visible":true,"origin":"","legend":"\u003cp\u003eA model for pathways of distribution and contamination of heavy metals in sediments from the dam at Changsha city along the Xiangjiang river in Hunan province, China.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/93b61534c8fc2500a88419c7.jpg"},{"id":97179330,"identity":"fe17e082-4fba-468e-9722-064626aaf01a","added_by":"auto","created_at":"2025-12-01 16:14:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6269636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/0d2f62cb-01e1-432d-8f60-d8d2d920e8f3.pdf"},{"id":89259526,"identity":"4d859e9b-ba69-45ce-9b34-6f2000dfc8db","added_by":"auto","created_at":"2025-08-18 06:28:02","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":536169,"visible":true,"origin":"","legend":"","description":"","filename":"Stable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7302369/v1/afe0a8ddc33720fecf60ae2b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distribution, contamination and source discrimination of heavy metals in sediments from dam reservoir at Changsha city along the Xiangjiang River, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe dam construction along river all over the world has expanded rapidly with the desire for clean electricity, irrigation, flood control, and urban development (Zhao et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Dam reservoir along river are typical sites where sediments accumulation is favoured (Vukovic et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), allowing their deposition and accumulation of heavy metals in sediments from both natural processes and anthropogenic activities (e.g., Fonseca et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003eb\u003c/span\u003e). It is suggested that over 90% of heavy metals in aquatic ecosystems can be adsorbed or precipitated by sediments (Zhao et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the concentrations of heavy metals in dam sediments increase after the construction, as observed in many dams such as the famous Three Gorges Reservoir in China (Bing et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Vaussaire dam in France (Fr\u0026eacute;mion et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), dam lakes in South Egypt (Darwish and P\u0026ouml;llmann, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and dam reservoir along the Ohře River of Czech Republic (Matys Grygar et al., 2018). This accumulation of heavy metals in dam sediments then may have negative impacts on the aquatic ecological environment of reservoir, which affects aquatic food safety and environmental quality (Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the heavy metal contamination in dam sediments has been a particularly important issue for the environmental protection and ecological safty of river water (e.g., Vergilio et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Almeida et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Dam sediments can deposit and concentrate heavy metals through dissolution, precipitation, and sorption with clays, organic matter (OM) and Fe/Mn oxides in reservior sediments, and the distribution of heavy metals in dam sediments is generally regulated by sediment composition (e.g., Vukovic et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, the Three Gorges Dam caused upstream sedimentary accumulation of heavy metals to be higher nearer to the dam than in the upper reaches, and pollutant content was sharply lower below the dam due to regulation of the spatial variation in sediment particle size (e.g., Xu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and OM and Fe/Mn oxide contents (e.g., Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cieśla and Gruca-Rokosz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, the distribution of heavy metals in dam sediments and its association with the sediment grain size, OM and Fe/Mn oxide contents (Fr\u0026eacute;mion et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and carbon, nitrogen, phosphorus, polycyclic aromatic hydrocarbons, and oxygenated PAHs contents (Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Due\u0026ntilde;as-Moreno et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have frequently been studied. Although the heavy metal contamination in dam sediments has been assessed (Almeida et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and its impact to the concentrations of heavy metals (e.g., As, Fe, Cd, Ni, and Zn) in reservoir water (Vukovic et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and (Cu, Fe, Zn and Hg) in aquatic biota (Sang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cieśla and Gruca-Rokosz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) has particuarly been elucidated, there is a lack of study focused on geochemistry of heavy metals in sediments from the dam reservoir trapped (post-dam) and those deposited in river bed on site before dam constructed (pre-dam) ( Zhu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., 2022). An effort of such a study is uncommon because of its cost and sampling opportunity (Pacheco et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As dam reservoir deposites may provide valuable sedimentary archives of heavy metals from both natural processes and anthropogenic activities (Yang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and heavy metals may a threat to water and human health (Zhu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Almeida et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Sreelesh et al., 2025); so it is essential to study the geochemistry of both the post-dam sediments (DS) and pre-dam fluvial sediments (FS) from a river to understand dam-induced changes in distribution and contamination of heavy metals in sediments.\u003c/p\u003e\u003cp\u003eThe Xiangjiang River, often referred to as the mother river of Hunan province, runs through the Changsha-Zhuzhou-Xiangtan urban area, a central hub for the province's economy, politics, and culture. Moreover, the dam is relatively closed to the urban area of Changsha. So the ecological environmental problems in this reservoir area are worthy of in-depth research. According to the average sedimentation rate of 2 cm/a in the downstream section of the Xiangjiang River (Audry et al., 2004), approximately 20 cm thick new sediments (DS, post-dam) had been deposited in the reservoir area during the past 10 year. The government had implemented pollution prevention and control measures in Xiangjiang River Basin over 10 year. But there are few reports on heavy metal pollution in Changsha dam sediment.\u003c/p\u003e\u003cp\u003eTherefore, it is possible to take a compare study on geochemistry of the post- and pre-dam sediments on site from this river (Fig.\u0026nbsp;1c). The present study contributes to this with a purpose to understand the distribution and contamination of heavy metals in the DS by (1) unearthing the geochemical variations between the DS and FS; (2) discriminating the trace metal sources in the DS using geochemical indicators on identifying natural sources from anthropogenic inputs; and (3) evaluating the contamination, and human health risk of heavy metals in the dam sediments.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eChangsha City is situated in the northeastern part of Hunan Province (Fig.\u0026nbsp;1a), 27\u0026deg;55\u0026apos;N to 28\u0026deg;41\u0026apos;N, 111\u0026deg;54\u0026apos;E to 114\u0026deg;15\u0026apos;E, its area has 1.18\u0026times;104 km\u003csup\u003e2\u003c/sup\u003e. The rock layeFS exposed throughout the Xiangjiang River basin are generally well-preserved, and granite rocks are widely distributed (Peng et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), but the research area is mainly composed of Quaternary sediments. The mudstone, red sandstone, and siltstone are the mainly origin rocks of sediment. This area is marked by a typical subtropical monsoon climate, with an annual average temperature ranging from 16.8 to 17.3\u0026deg;C and yearly precipitation between 1359 and 1553 mm. Before the implementation of pollution prevention and control projects, the upstream areas of the study area, including Shuikoushan in Hengyang, Xiawan bay in Zhuzhou(ZMP), Yuetang in Xiangtan(XSC), and Zhubu bay(ZCD), were important industrial and mining areas within the Xiangjiang River Basin (Li et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previously, industrial and mining enterprises had a long history of development, and a considerable quantity of industrial waste was released into the Xiangjiang River, causing the sediment in Changsha section to receive various pollutants from the upstream basin over an extended period (Chai et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). After the dam construction, heavy metals may be more easily enriched in reservoir area (Bing et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Sampling and sediment samples\u003c/h2\u003e\n \u003cp\u003eThe Xiangjiang Changsha comprehensive dam is located in Caijiazhou, Wangcheng District, Changsha City (Fig.\u0026nbsp;1b). The ninth level of a cascade dam in Xiangjiang River. The main task is to secure the production and domestic water supply for the urban agglomeration of Changsha, Zhuzhou, and Xiangtan, adapt to the construction of waterfront landscape belts, enhance the navigation conditions of the waterway in the Changsha-Zhuzhou-Xiangtan stretch, and take into account functions such as power generation. The sampling of dam sediments (DS) was completed in November 2022. This work used a grab type mud collector to collect 44 sediment samples, and they are named YP (Yinpenling Bridge), FY (Fuyuan Road Bridge), SW (Wangcheng), and JG(Jing gang) respectively. The specific location distribution is shown in Fig.\u0026nbsp;1b. To obtain more representative surface sediment, a sample was taken every 200 m at each sampling point. All samples gathered are stored in sealed plastic bags and transported to the laboratory for analysis. The sampling of river sediment (FS, pre-dam) was completed by our research group in November 2010, with specific methods referring to Peng et al. (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe newly collected DS sediment samples are about 20 cm thick, all of which are brownish yellow silty silt sediments (Fig. 1c). The upper layer of FS sediment (0-20cm) has a lighter color, mainly consisting of brownish yellow and light-yellow silty silt layeFS. The lower layer (\u0026gt;\u0026thinsp;50cm) is brown and black silty silt, with no mica fragments or plant branches observed (Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Analyses of elements\u003c/h2\u003e\n \u003cp\u003eThe analysis of the primary elements in the DS and FS sediments from the Xiangjiang reservoir area was conducted using a PW2404 X-ray fluorescence analyzer (XRF) located at the State Key Laboratory of Isotope Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. The chemical treatment of samples, instrument working conditions, analysis accuracy, error and standard samples were referred to the relevant photographic literature (Fang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHeavy metal concentrations in DS sediment samples were analyzed at the Key Laboratory of Isotopic Geochemistry, part of the Chinese Academy of Sciences. Heavy metals in dissolved samples were measured over a broad concentration range using an Elan 6000 inductively coupled plasma mass spectrometer (ICP-MS). For detailed analysis methods, please refer to Fang et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The 1M HCl extractants from samples labeled with S and R suffixes were analyzed using a Perkin Elmer Optima 5300 ICP-MS at the Future Industries Institute, University of South Australia. The results generally deviated by no more than \u0026plusmn;\u0026thinsp;7% from the certified values, with an analytical accuracy exceeding 5% for the measurements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Enrichment, contamination and ecilogical risk assessmenrt methods\u003c/h2\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.1 Enrichment of trace metals\u003c/h2\u003e\n \u003cp\u003eThe enrichment degree of heavy metals was assessed using the enrichment factor (EF), calculated by Eq.\u0026nbsp;(3). This factor represents the ratio of the double-normalized target metal A to the reference element A\u003csub\u003eRef\u003c/sub\u003e in a sample, compared to the background concentration. The definition of EF aligns with the assumption that metal A and A\u003csub\u003eRef\u003c/sub\u003e in a sample exist within finely crystalline secondary phases (such as silt and clay), which are diluted by quartz particles, and are released from A and ARef (Bing et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). So, this article selects Al as the reference element (Peng et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003eEF=(X/A)sample/(X/A)background (1)\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.2 Assessment of heavy metal contamination and ecological risk\u003c/h2\u003e\n \u003cp\u003eThe degree of heavy metal contamination in sediments was assessed using the Geoaccumulation index (I\u003csub\u003eGeo\u003c/sub\u003e) by eliminating the influence of geological contribution. For this, the background concentration of each metal and a correction coefficient K were included in the assessment, expressed as Eq.\u0026nbsp;(2):\u003c/p\u003e\n \u003cp\u003eI\u003csub\u003eGeo\u003c/sub\u003e = Log\u003csub\u003e2\u003c/sub\u003e [C\u003csub\u003eM\u003c/sub\u003e / (K \u0026times; B\u003csub\u003eM\u003c/sub\u003e)] (2)\u003c/p\u003e\n \u003cp\u003ewhere C\u003csub\u003eM\u003c/sub\u003e is the concentration of the evaluated metal M in a sample and B\u003csub\u003eM\u003c/sub\u003e is the LBV of that metal. Thus, the most important step for the assessment is the definition of K. Here, K value was determined by normalizing the concentration of Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e in the samples to the LBV (12.8%) (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), as shown in Eq.\u0026nbsp;(3):\u003c/p\u003e\n \u003cp\u003eK = (C\u003csub\u003eAl2O3\u003c/sub\u003e)\u003csub\u003esample\u003c/sub\u003e / (C\u003csub\u003eAl2O3\u003c/sub\u003e )\u003csub\u003eLBV\u003c/sub\u003e (3)\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.3 Assessment model of human health risk\u003c/h2\u003e\n \u003cp\u003eWe use the health risk model proposed by USEPA (USEPA, 2014) to evaluate the non carcinogenic risks of heavy metals (Cd, Ni, Cu, Pb, Zn, Mn) to humans in sediments of the Xiangjiang Reservoir area:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHI = HQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eing\u003c/em\u003e +\u003c/sub\u003e \u003cem\u003eHQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eder\u003c/em\u003e +\u003c/sub\u003e \u003cem\u003eHQ\u003c/em\u003e\u003csub\u003e\u003cem\u003einh\u003c/em\u003e\u003c/sub\u003e (4)\u003c/p\u003e\n \u003cp\u003eIn the above equation, HQ\u003csub\u003eing\u003c/sub\u003e, HQ\u003csub\u003eder\u003c/sub\u003e, and HQ\u003csub\u003enah\u003c/sub\u003e represent the non carcinogenic risk quotient for ingestion, skin contact, and inhalation, respectively; HI represents the non carcinogenic risk factor value (Jafarabadi et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sreelesh et al., 2025).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Source analysis of heavy metals (APCS-MLR model)\u003c/h2\u003e\n \u003cp\u003eThe APCS-MLR model works by deriving the rotation factor loading matrix and eigenvalues through principal component analysis (PCA),and calculating the principal factor eigenvector. To assess the contribution of identified pollution sources to the substances in the receptor, multiple linear regression (MLR) was performed using feature vector and absolute principal component scores (APCS) obtained from standardized data (Lv et al.,2019; Peng et al., 2024). The formula is\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\n \u003cp\u003ePearson correlation analysis and Principal component analysis (PCA) are employed to examine the relationships between major and heavy metals. Additionally, PCA and derivation techniques can help identify potential sources of heavy metal pollution in sediments(Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). A KMO value greater than 0.5 and Bartlett tests (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) serve as criteria to assess the validity of the PCA results and the variance matrix for heavy metals in the sediments (Chai et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhuang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). SPSS 18.0 for Windows was used to perform all statistical analyses.\u003c/p\u003e\n \u003cp\u003eThe Shapiro test was applied to assess the normality of the distribution for major elements and heavy metals, including concentrations, enrichment coefficients, and ground accumulation index for DS/FS sediment (Zhuang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The equality of variance and the form of the t-statistic were determined using the f-test. For elements that are not normally distributed, the non-parametric Mann-Whitney test was applied.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Distribution of major and trace elements in sediments\u003c/h2\u003e\n \u003cp\u003eConcentrations of major and trace elements in the DS and FS were reported in Supplemantary STable 1, in which relative data of the UCC (Gao et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), NASC (Gromet et al., \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e), HGR(Yan et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), China soils (CAS, Yan et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), Yangtze river sediments (YZ, Yan et a., 1997), and XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) were included for comparison. The distribution features of major and trace elements in sediments are summarized below.\u003c/p\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Major elements\u003c/h2\u003e\n \u003cp\u003eConcentrations of major elements SiO\u003csub\u003e2\u003c/sub\u003e, TiO\u003csub\u003e2\u003c/sub\u003e, Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, MnO, MgO, CaO, K\u003csub\u003e2\u003c/sub\u003eO, Na\u003csub\u003e2\u003c/sub\u003eO, P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e, and LOI in the DS (n\u0026thinsp;=\u0026thinsp;52) were around 69.5, 0.68, 12.59, 5.61, 0.23, 1.03, 0.91, 2.2, 0.44, 0.17, and 6.52 (wt.%) respectively, with Cv (coefficient of variation)\u0026thinsp;\u0026lt;\u0026thinsp;0.2. Such a major element composition of the DS was comparable to that of the UCC (Gao et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), NASC (Gromet et al., \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e), CAS (Yan et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), YZ (Yan et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), and XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), as shown by plots of SiO\u003csub\u003e2\u003c/sub\u003e% vs. oxide concentrations (Fig.\u0026nbsp;2). Also, Fig.\u0026nbsp;2 displayed that SiO\u003csub\u003e2\u003c/sub\u003e concentrations were negatively linarly correlated to those of TiO\u003csub\u003e2\u003c/sub\u003e (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.53), Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.95), Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.82), MnO (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.62), MgO (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.52), P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.56) and LOI (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.94) (Fig.\u0026nbsp;2a-e, I, j), and positively to Na\u003csub\u003e2\u003c/sub\u003eO (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.71) (Fig. 2g), showing significant grain size effect on element concentration (Fang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Concentrations of mobile elements CaO (around 0.91 wt.%), K\u003csub\u003e2\u003c/sub\u003eO (2.2 wt.%), and Na\u003csub\u003e2\u003c/sub\u003eO (0.44 wt.%) in the DS were significantly lower than that of the UCC (Gao et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), NASC (Gromet et al., \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e), and HGR (Yan et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) (STable 1, Fig. 2f-h), suggesting lost of these elements through leaching during source rock weathering (Sharma et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). This rock weathering was in moderate to high degree as indicated by CIA (Nesbitt and Young, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e) values ranging from 67.5 to 82.4 (Fig. 3a), and it represented one of the major natural processes that determined the element composition of the DS (Chen et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn general, the hydrological differentiation and chemical weathering processes that operate on river sediments lead to an increase in quartz at the expense of feldspar, mafic minerals, and lithic fragments in the river sediments (Singh \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), and then result in an increase in the sediment maturity (Singh \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, the plot of log(SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) vs. log(Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e/K\u003csub\u003e2\u003c/sub\u003eO) (Singh \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) was used to characterize the chemical maturity of the DS, it displayed that the DS samples occupied a field from litharenite through wacke to shale (Fig. 3b), showing the chemical immaturity of the DS. This chemical immaturity can also be indicated by the ICV values (0.82\u0026ndash;1.25) that were higher than those of matured clays (0.03\u0026ndash;0.78) (Mclennan et al., \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e). Therefore, the DS might have suffered from the upper river source lithological differences (Fig. 1a) after less well hydrological sorting (Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMore importantly, the FS had major element compositions very similar to that of the DS (Fig.\u0026nbsp;2, Fig.\u0026nbsp;3b), as also indicated by their similar CIA (around 75) and ICV (around 0.92) values (Fig.\u0026nbsp;3b, STable 1).\u003c/p\u003e\n \u003cp\u003eHowever, the DS had SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e ratios (6.34\u0026ndash;16.9, average\u0026thinsp;=\u0026thinsp;9.86) slightly higher than the FS (5.15\u0026ndash;16.4, average\u0026thinsp;=\u0026thinsp;7.87), and the SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e ratios of the DS significantly decreased from the upper YP and FY sediments (6.3\u0026ndash;16.9, average\u0026thinsp;=\u0026thinsp;10.5) to the near dam SW and JG sediments (6.34\u0026ndash;9.4, average\u0026thinsp;=\u0026thinsp;7.5). While, those of the FS veried oppositely, with upper HG and JZ sediments (5.15\u0026ndash;9.8, average\u0026thinsp;=\u0026thinsp;6.78) being slightly lower than that of the lower river SG and XW sediments (5.15\u0026ndash;16.4, average\u0026thinsp;=\u0026thinsp;9.1 (STable 1). As SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e ratio represents the content of detrital minerals (e.g., quartz) relative to clays (e.g., kaolinite) in sediments, it is commonly used to qualtify the sediment grain size variation (Bbek, et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Greber and Dauphas, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, the distinct decrease of SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e ratios from the upper river to near dam in the DS indicated significantly the decrease of hydrological dynamics for sediment deposition due to dam construction (Fig. 1b). While, the deposition dynamic setting for the pre-dam fluvial system varied oppositely probably due to the river bed slope. Therefore, the DS resulted from the deposition dynamics slightly different from that of the pre-dam FS.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Trace metals\u003c/h2\u003e\n \u003cp\u003eTrace metals including Ni, Mn, Cu, Zn, Pb, Cd, Co, V, Cr, Th, Zr, and Hf in the DS had distinctly variable concentrations (Cv\u0026thinsp;\u0026gt;\u0026thinsp;0.2), while those of the rest (Ba, Sc, U, Tl, Cs, Ga, Ge, Rb, Sr, Nb, Ta, and REE) were less variable (Cv\u0026thinsp;\u0026lt;\u0026thinsp;0.2). It is seen that heavy metals Mn, Ni, Cu, Zn, Pb, and Cd in the DS had concentrations (STable 1) significantly higher than the XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) as shown in Fig. 4, in which Ni and Cd had concentrations ranging from 23.67 to 7699 (mg/kg) and 0.28 to 25.9 (mg/kg), with averages of 494 and 3.32 (mg/kg), respectively (Fig. 4a, b). Also, sample CS1 had extremely higher concentrations of Cd (25.9 mg/kg), Pb (162.2 mg/kg), and Zn (1080.8 mg/kg) than other samples (Fig. 4d). However, other trace metals (except Zr and Hf) in the DS generally had concentrations comparable to that of the XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The EF values showed that heavy metals (Mn, Ni, Cu, Zn, Pb, and Cd) were significantly enriched (with EF\u0026thinsp;\u0026gt;\u0026thinsp;1.5), while otheFS (except Zr and Hf) were neither enriched nor depleted (EF around 1.0) in the DS (Fig. 5a). The different distribution patterns between heavy metals (Mn, Ni, Cu, Zn, Pb, and Cd) and other trace metals in the DS may suggest that these heavy metals had sources different from other trace metals (e. g., Darwish et al., 2018; Due\u0026ntilde;as-Moreno et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIt is interesting to find that the distribution of trace metals (except Ni) in the FS was very similar to that in DS, as shown by metal concentrations (Fig. 4a-c) and the EF values (Fig. 5a, b). Concentrations of some terrigenous pair metals such Zr-Hf, Nb-Ta, Ga-Ge, Rb-Sr, Rb-Cs, and Nd-Sm in the FS and DS were positively linear correlated (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.50) to each other, with ratios such as Zr/Hf, Nb/Ta, Ga/Ge, Rb/Sr, Rb/Cs, and Nd/Sm of the FS equal to that of the DS (STable 1). For example, Zr/Hf ratios of the FS ranged from 32.3 to 36.9 with an average of 34.7, being equal to those (33.5 to 36.8, average\u0026thinsp;=\u0026thinsp;34.8) of the DS. The similar ratio values of these pair metals in the DS and FS not only informed that these pair metals behaved similarly during weathering, transporting, and deposition processes, but also indicated that the DS and FS resulted from the similar terrigenous sources (Singh, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), because these terrigenous trace metals are generally hosted in deterital minerals, such as Zr in zircon, Rb, and Cs in clay (Fang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the natural processes in the water for the DS deposition after 2014 worked the same to the FS deposition before 2011, and they had impacted less on the differences of trace metal distribution in the DS and FS.\u003c/p\u003e\n \u003cp\u003eMore importantly, the DS had REE distribution patterns very similar to the FS. Concentrations of total REE (\u0026sum;REE) in the DS ranged from 182.5 to 343.4 (mg/kg) with an average of 275.9 mg/kg (n\u0026thinsp;=\u0026thinsp;52), being comparable to that of the FS that had \u0026sum;REE ranging from 148.1 to 531.1 (mg/kg) with an average of 295.2 mg/kg (n\u0026thinsp;=\u0026thinsp;95) (STable 1). Also, the DS displayed two types of REE distribution patterns, the V-shape and flat\u0026ndash;shale types (Fig.\u0026nbsp;6). Both REE types had (La/Yb)\u003csub\u003eN\u003c/sub\u003e, (La/Sm)\u003csub\u003eN\u003c/sub\u003e, (Gd/Yb)\u003csub\u003eN\u003c/sub\u003e, and Ce/Ce\u003csup\u003e*\u003c/sup\u003e ratios around 1.28, 1.13, 1.11, and 0.92 respectively, with Eu/Eu\u003csup\u003e*\u003c/sup\u003e values around \u0026minus;\u0026thinsp;0.16 for the V-shape and 0.07 for the flat-shale type (Fig. 6a, STable 1). Such a REE distribution pattern was very similar to that of the FS (Fang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This provides a further support to the above conclusion, and natural processes taking place in the water had resulted in a limited variation on trace metal distribution in the DS and FS.\u003c/p\u003e\n \u003cp\u003eMoreover, ratio of terrigenous pair metals such as Zr/Hf, Nb/Ta, Ga/Ge, Rb/Sr, Rb/Cs, and Nd/Sm in the DS and FS were comparable to that of the UCC (Gao et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) and XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) (STable 1). This indicates that the terrigenous compositions of the DS and FS were resulted from averaging of the upper river source rocks (sedimentary rocks and granites) through weathering, hydrological sorting, and mixing (Sharma et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Difference of metal distribution between the post- and pre-dam sediments\u003c/h2\u003e\n \u003cp\u003eAlthough the DS and FS were characterized by similar major (Figs. 2, 3) and trace (Fig. 5, 6) element compisitions, the normality via the Shapiro-Wilk method (Perkins and Mason, 1995) suggested that trace metals determined to be non-normally distributed in at least one sample population included Ni, Mn, Zn, Cd, Cu, and Pb (Fig. 4a-c). Significant differences (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05) exist for these six metals with the higher mean and median concentrations for all elements in both the DS and FS. The difference with respect to Ni was the result of the singularly high concentrations ranging from 23.67 to 7699.5 (mg/kg) with an average of 449.5 mg/kg (n\u0026thinsp;=\u0026thinsp;52) in the DS, being distinctly higher than that of the FS (from 24.75 mg/kg to 93.45 mg/kg with an average of 48.7 mg/kg, n\u0026thinsp;=\u0026thinsp;95) (Fig. 4a). Statistical results suggested that Ni concentration in the DS had an increase of 915% (p\u0026thinsp;=\u0026thinsp;0.0004) relative to the FS (Fig. 4a), and there were 69.2% of DS samples containing Ni concentrations higher than its LBV (48.5 mg/kg) of the XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ni was scattered in the DS with the higher concentrations in upper river YP and down river JG sediments (Fig.\u0026nbsp;4d), and no grain size effect on Ni concentration was found (Ni concentrations in the DS were less significantly correlated to SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e ratios, see below). While, such a high Ni concentration in the DS had rarely been observed in the past not only in this river (e. g., Peng et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), but also in many otheFS around the world. For our knowledge, only the high concentration of Ni (330 mg/kg) in sediments was found at the Panzihua city of the Yangetze river in China (Wu et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), where Ni-bearing ores were exploited (Wu et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). While, Ni concentrations in many dam sediments were low, for example, sediments from the Kafrain dam had Ni concentration in average of 170 mg/kg (Zhao., 2024), Kapulukaya Reservoir 65.8 mg/kg (Kankılı\u0026ccedil; et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), Iron Gate Reservoir 74.5 mg/kg (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Antweiler and Vaussaire Reservoir 58.3 (mg/kg) (Fr\u0026eacute;mion et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), being much lower than those in the DS of this study. Thus, it is particularly essential to strengthen such an extremely high Ni concentration in the DS.\u003c/p\u003e\n \u003cp\u003eThe difference with respect to Mn, Zn, Cd, Cu, and Pb was the result of a relatively lower concentrations in the DS (except in sample SC1, STable 1). Statistical results suggested that concentrations of these metals in the DS had a decrease of about 23% (p\u0026thinsp;=\u0026thinsp;0.0001), 49% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), 76% (p\u0026thinsp;=\u0026thinsp;0.00003), 34% (p\u0026thinsp;=\u0026thinsp;0.00001), and 49% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) respectively, relative to that in the FS (Fig. 4a-c). This is quite different from other observations which found that concentrations of trace metals in dam sediments were increased relative to the fluvial sediments (e.g., Bing et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). As the natural processes in the wateFShed affected less on trace metal distribution in the DS and FS, this concentration decrease was probably resulted from the remove of industrial plants such as the ZMP, STC, and ZCD (Fig. 1b) in lower reaches, where the non-ferrous minerals and ores were once smelted and refined therein (Peng et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, the difference of trace metal distribution between the DS and FS was characterized by heavy metals Ni, Mn, Zn, Cd, Cu, and Pb\u003c/p\u003e\n \u003cp\u003eMany studies have concluded that the accumulation and contamination of heavy metals in dam sediments were regulated by the spatial variation in sediment particle size, OM and Fe/Mn oxide contents (Audry et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fr\u0026eacute;mion et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), and other chemical components (e.g., Guo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cieśla and Gruca-Rokosz, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, this study on compaFSion of geochemical composition and pair metal ratio of the DS and FS suggests that heavy metal distribution an contamination in the DS were resulted from anthropogenic inputs of these metals, which were less associated with the spatial variation in particle size (SiO\u003csub\u003e2\u003c/sub\u003e/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e ratio), OM (content of LOI) and Fe/Mn oxide contents in the DS. Thus, the distribution of heavy metal contamination in dam sediments was mainly resulted from anthropogenic activities in the water.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Source discrimination of trace metals in dam sediments\u003c/h2\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Dam impacting on the heavy metals anthropogenic sources input\u003c/h2\u003e\n \u003cp\u003ePCA and Pearson\u0026apos;s correlation analysis are frequently applied to discriminate the trace metals of natural sources from those of anthropogenic inputs for understanding and assessing the heavy metal contamination in sediments (e.g., Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Major elements in sediments represent the definite mineralogical compositions of the sediments, such as SiO\u003csub\u003e2\u003c/sub\u003e for silicate minerals (e.g., quartz, feldsapr, etc.), and Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e the clay (e.g., Sharma et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, the association of trace metal with major elements is applied to determine at least the host mineral phases of trace metals in sediments and then to disctiminate their sources (Fok et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Here, PCA was performed based on the Al-normalized concentrations of major and trace elements in the DS samples (n\u0026thinsp;=\u0026thinsp;52). The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) value for the PCA was 0.718 (\u0026gt;\u0026thinsp;0.6), and the associated probability of the Bartlett sphericity test was 0. That indicates that PCA could be applied to the dimensionality decompositions (Sreelesh et al., 2025). The PCA results revealed that the variability of major and trace elements could be expressed as three principal components (PC1, PC2, and PC3) that explained 74.052% of the total variance with relative contributions of 51.018%, 12.331%, and 10.724% respectively. These data were plotted in a PC1 vs. PC2 vs. PC3, plot (Fig.\u0026nbsp;7) to visualize the two groups of elements. Group A that had loadings from \u0026minus;\u0026thinsp;0.6 to 0.8 for PC1, PC2, and PC3 included heavy metals Mn, Zn, Cd, Cu, Pb, Ni, Co, V, Cr, and Sc, and major elements Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, MnO, P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e, and LOI. Group B that had loadings from \u0026minus;\u0026thinsp;0.2 to 0.8 for PC1, -0.7 to -0.4 for PC2, and \u0026minus;\u0026thinsp;0.4 to 0.4 for PC3 included the rest of trace metals, and major elements SiO\u003csub\u003e2\u003c/sub\u003e, TiO\u003csub\u003e2\u003c/sub\u003e, CaO, MgO, K\u003csub\u003e2\u003c/sub\u003eO, and Na\u003csub\u003e2\u003c/sub\u003eO (Fig.\u0026nbsp;7). This grouping scheme matches the concentration variation and enrichment (EF values) features of major and trace elements in the DS (Fig.\u0026nbsp;5a, b, STable 1). The Pearson\u0026apos;s correlation analysis (STable 1) suggested that metals Ni, Mn, Zn, Cd, Cu, and Pb were significantly postively correlated to each other (r\u0026thinsp;\u0026gt;\u0026thinsp;0.52, p\u0026thinsp;=\u0026thinsp;0.001), and they (except Cd, and Ni) were also positively correlated to Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e (r\u0026thinsp;\u0026gt;\u0026thinsp;0.55, p\u0026thinsp;=\u0026thinsp;0.001), Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e (r\u0026thinsp;\u0026gt;\u0026thinsp;0.43, p\u0026thinsp;=\u0026thinsp;0.001), MnO (r\u0026thinsp;\u0026gt;\u0026thinsp;0.59, p\u0026thinsp;=\u0026thinsp;0.001), and LOI (r\u0026thinsp;\u0026gt;\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.001). As metal Cd was significantly positively correlated to Zn (r\u0026thinsp;=\u0026thinsp;0.99, p\u0026thinsp;=\u0026thinsp;0.001) and Pb (r\u0026thinsp;=\u0026thinsp;0.81, p\u0026thinsp;=\u0026thinsp;0.001), and Ni correlated to Cu (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.001) (STable 1), it is suggested that these heavy metals were hosted in clays, Fe/Mn oxide minerals, and OM in the DS. Thus, heavy metals that had higher concentrations than other metals (Fig. 4, STable 1) and were significantly enriched (Fig. 5) in the DS were resulted from additional contribution, the anthropogenic activities (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). While, other trace metals Co, V, Cr and Sc in Group A, and those in Group B (Fig.\u0026nbsp;7) that were correlated to silicate components SiO\u003csub\u003e2\u003c/sub\u003e, TiO\u003csub\u003e2\u003c/sub\u003e, MgO, K\u003csub\u003e2\u003c/sub\u003eO, and Na\u003csub\u003e2\u003c/sub\u003eO and had concentrations comparable to the LBV of the XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) with EF values around 1.0 (Fig. 5a) were mostly of the natural sources (Dhivert et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wu et al, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe sources of heavy metals in fluvial sediments of the river have been documented by many studies, for which the anthorpogenic contribution of heavy metals has been attributed to the mining activities (e.g., smelting, refining, etc.) popuparized in the watershed (e.g., Peng et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sun et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Peng et al., \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e). Although the implementation of the environmental protection and ecological conservation project (Chai et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) has led to reduce the metal sources by removing industrial plants such as the ZMP, XSC, and ZCD in lower reaches (Fig. 1b), the mining activities has less been terminated in upper river areas due to the need of economic development. Thus, mining activities in upper river areas represent the major sources of heavy metals as before (Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The significant concentration decrease of the mine induced metals in the DS (Fig. 4a-c) indicates that (1) removing the major industrial plants in the lower reaches has taken effect on environmental protection and ecological conservation for the watershed; and (2) Ni with high concentrations in the DS was not possibly contributed from mining activities in upper river areas. Moreover, ratios of Ni/Co, Ni/Cr, and Cr/Co in the FS were definitely around 2.46, 0.52, and 4.77 respectively (STable 1), which were comparable to that of the UCC (2.24, 0.48, and 4.7 respectively) (Gao et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) and XJR (2.39, 0.53, and 4.5 respectively) (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), illustrating that Ni as well as Cr and Co in the FS was contributed from source rocks of the watershed (Fang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, ratios of Ni/Co and Ni/Cr in the DS ranged uncertainly from 1.4 to 324 and 0.36 to 15.6 respectively, being quite different from that in the FS. While, the Cr/Co ratios in the DS (except sample YP9 and YP19) were defintiely around 4.77, being comparable to that of the FS, UCC (Gao et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) and XJR (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). That again suggests that Ni in the DS had source contribution different from metals Co and Cr in the DS and Ni in the FS, and metals Co and Cr in the DS were similarly and naturally contributed from source rocks as those were in the FS (Singh, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Peng et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe higher Ni concentrations (up to 7699.5 mg/kg) in the DS were observed in the YP sediments at Yinpengling district where the Chinese catering was concentrated along the dam river since 2014 and in the JG sediments at the Jinggang village where distributed the shipping ports. Samples (n\u0026thinsp;=\u0026thinsp;22) between the YP and JG sediments except sample SW6 and YP6 that Ni concentrations of 737.9 and 145.3 (mg/kg) respectively, generally had the Ni concentrations around 46 mg/kg (Fig. 1b, 4d). As metal Ni is less mobile in surface system (Liu et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), and it is usually used for ceramic colour in catering and corrosion protection coating for shipes (Peluso et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Guo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), the spatial distribution of Ni in the DS (Fig.\u0026nbsp;4d) implies that the waste discharges from catering and shipping activities in the dam reservoir may represent the major sources of Ni in the DS. Therefore, pathways for heavy metals in the DS included those from natural process (e.g., rock weathering), mining activities in upper river areas, and catering and shipping activities within the dam reservoir. The natural processes led to the fundmental distribution of heavy metals in the DS, and the anthropogenic activities in upper river areas and within the dam led the changes of trace metal distribution patterns (Fig.\u0026nbsp;4, and 5a, b) and caused the heavy metal contamination (Fig.\u0026nbsp;9).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Analysis of pollution sources based on APCS-MLR model\u003c/h2\u003e\n \u003cp\u003eWe initially assessed the contribution of various pollution sources to the heavy metals present in the sediments and computed the absolute principal component scores for each sample. The overall variance was 0.048. The results of this analysis were reliable, as evidenced by the error rate between the measured and predicted concentrations of most elements being less than 1%(Peng et al., 2024). Finally, using Eq.\u0026nbsp;(5), the influence of each pollution factor on the sediment\u0026apos;s heavy metal content was assessed, as shown in Fig.\u0026nbsp;8.\u003c/p\u003e\n \u003cp\u003eNatural sources accounted for 40.7% of total contribution in DS sediment, and was loaded with Ba (67.96%) ,Sc (70.94%),V (93.14%), Co (67.93%), Th (61.39%) and U (79.86%). Mn, Zn, Pb, Cd were loaded in industry sources, with the loading as following in order 43.06%, 61.52%, 44.88%, 86.92%. Agricultural and catering sewage sources had 17.5% accounts for the sediment total contribution, and heavy metals Cr, Ni, Cu had high loading. Tl was affected by mixed sources of 46.92%.\u003c/p\u003e\n \u003cp\u003eThe construction of dams appeared to affect the loading rate of certain heavy metal pollution sources. For example, natural sources had the highest contribution for heavy metal Ni in FS sediment, and metals Mn, Zn, Pb etc. were higher loaded on industry sources in FS sediment (Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Meanwhile, after dam construction, agricultural and domestic sewage sources and mixed sources contribution in sediment seems to be increased; but industry sources contribution in sediment decreased from 42.31\u0026ndash;31.1% (Fang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Dam construction may accelerate the massive accumulation of agricultural and catering pollutants (Fremion et al., 2016; Guo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), which lead to the changes of construction. The changes of industry sources contribution in sediment may mainly relate to pollution prevention and control projects by government (Gao et al., \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The outcomes of the APCS-MLR model aligned with the findings from the related PCA analysis (Fig. 7).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Contamination of heavy metals in dam sediments\u003c/h2\u003e\n \u003cp\u003eThe I\u003csub\u003eGeo\u003c/sub\u003e values of heavy metals calculated using Eqs. (2) and (3) were summarized in STable 1 and displayed in Fig. 5c, and they showed that Cu, Zn, and Pb represented less significant contamination in the DS (I\u003csub\u003eGeo\u003c/sub\u003e \u0026lt; 1.5), with only 13.5% samples being slightly and moderately contaminated in Zn (1.5\u0026thinsp;\u0026lt;\u0026thinsp;I\u003csub\u003eGeo\u003c/sub\u003e \u0026lt; 2.5). Thus, contamination of Cu, Zn, and Pb in the DS was insignificant althrough sample SC1 had high concentrations of Zn and Pb (Fig. 4d). Mn and Cd represented moderate level of contamination (1.5\u0026thinsp;\u0026lt;\u0026thinsp;I\u003csub\u003eGeo\u003c/sub\u003e \u0026lt; 2.5) with about 26.9% samples being extremely contaminated in Cd (I\u003csub\u003eGeo\u003c/sub\u003e \u0026gt;4.0). It is seen that contamination level of metals Cu, Zn, Pb, Mn, and Cd in the DS was significantly lower than that of the FS (STable 1). While, Ni represented a moderate to high level of contamination, for which about 44.2% samples was highly contaminated (1.5\u0026thinsp;\u0026lt;\u0026thinsp;I\u003csub\u003eGeo\u003c/sub\u003e \u0026lt; 4.0) and 25% samples extremely contaminated in Ni (I\u003csub\u003eGeo\u003c/sub\u003e \u0026gt;4.0). Thus, the contamination of Ni in the DS was very serious.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Assessment of Human Health Risk Models\u003c/h2\u003e\n \u003cp\u003eTo assessment the potential health risks of heavy metals in dam sediment, both dermal and ingestion contact had been used as a primary exposure pathways to child and adult. The findings summarized in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, indicating that except for Ni and Cd in some samples(HI\u0026thinsp;\u0026gt;\u0026thinsp;1), the HI values of other heavy metals (Mn, Cu, Zn, Pb)for adults and child are within an acceptable range (HI\u0026thinsp;\u0026lt;\u0026thinsp;1). The non carcinogenic risk points of Ni for adult and child are mainly in YP9 (HI\u0026thinsp;=\u0026thinsp;3.2389/4.9751), YP18 (HI\u0026thinsp;=\u0026thinsp;1.6871/2.5346), and YP19 (HI\u0026thinsp;=\u0026thinsp;2.5579/3.0654). The non carcinogenic risk of Cd to adult and child is mainly located in SC1 (HI\u0026thinsp;=\u0026thinsp;1.2579/2.4587). Based on the above analysis, it can be concluded that the overall non carcinogenic risk of heavy metals in the sediments of the Xiangjiang Reservoir area is low. However, due to the non carcinogenic risk of Ni and Cd to humans, especially child, in some sediments, it is necessary to strengthen the control of these two elements in the process of pollution prevention and control in the reservoir area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Formation processes of heavy metal contamination in dam sediments\u003c/h2\u003e\n \u003cp\u003eThe source disticmination by PCA, Pearson\u0026apos;s correlation analysis and APCS-MLR model in this study suggested that heavy metals in the DS were contributed from natural processes and anthropogenic activities, and those from anthropogenic activities such as mining activities in upper river areas, and catering and shipping activities within the dam reservoir led to the heavy metal contamination and then impacted to the ecological system of the waters. Contamination of Cd, Mn, Cu, Pb and Zn in DS resulted from inputs of heavy metals from mining activities in upper river areas, while this of the Ni in the DS was associated with waste discharges from catering and shipping activities within the reservoir(Fleischmann et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, the distribution and contamination of heavy metals in the DS can be summarized in Fig. 9, and attentions should be paid further to manage the wastes discharges from mining activities in upper river areas and from catering and shipping activities within the reservoir .\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e(1) The dam sediments deposited after 2014 had the major and trace element compositions similar to that of the pre-dam fluvial sediments deposited on site before 2011. Major elements Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, MnO, P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e and LOI, and trace metals Mn, Zn, Cd, Cu, Pb, and Ni had variable concentrations and were enriched in the sediments. While, major elements SiO\u003csub\u003e2\u003c/sub\u003e, TiO\u003csub\u003e2\u003c/sub\u003e, CaO, MgO, K\u003csub\u003e2\u003c/sub\u003eO and Na\u003csub\u003e2\u003c/sub\u003eO, and other terrigenous trace metals (Ba, Sc, Tl, Cs, Ga, Ge, Rb, Sr, Nb, Ta, Zr, Hf, and REE) in the sediments had concentrations less variable and comparable to the source rocks.\u003c/p\u003e\u003cp\u003e(2) Differences of trace metal distribution in dam sediments was characterized by a distinct increase of Ni concentration and decreases of concentrations of Mn, Zn, Cd, Cu and Pb relative to the pre-dam fluvial sediments. The dam sediments were contaminated in these metals, with contamination for Ni and Cd in high degree and others in slight degree. The contamination of Cd and Ni in the dam sediments presented a high level of potential ecological risk for the reservoir waters.\u003c/p\u003e\u003cp\u003e(3) It is worth noting that metals Ni and Cd have non carcinogenic risk for adult and child in some samples of dam sediment. As heavy metals from wastes like mining activities in upper river areas may led to the contamination of Mn, Zn, Cd, Cu, and Pb in the sediments, but catering and shipping activities within the reservoir may be the main cause of the Ni contamination in sediments. So protection for heavy metals (especially Ni, Cd) contamination in the dam reservoir should pay a great attention to the anthropogenic activities both in upper river areas and within the dam reservoir.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e This study was financially supported by the Construction Program for Fish-Class Disciplines (Geography-5010002) of Hunan Province (China), and the Key Project of Development Biology \u0026amp; Breeding (2022XKQ0207) from Hunan Province (China). Part of the work was also supported by the Scientific Research Funds of Hunan Provincial Education Department (Grant No. 18A012) and grants (Grant No. 2022JJ30030) from Department of Science and Technology of Hunan province (China). Associate Prof. Xianglin Tu at the Guangzhou Institute of Geochemistry, Chinese Academy of Science is thanked for helping for analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBo peng contributed to the Writing\u0026mdash;original draft, conceptualisation, data curation. Xia yang formal analysis, Methodology, and Investigation. Si cheng Wu contributed to the visualisation and Software. Nengqiu Wu, Hongjie Hu and Xianjia Du \u0026nbsp;contributed to reviewing and supervision. Yunhan Qu, Yanan Dai and Xin Wang contributed to reviewing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Data will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of interest The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida, T. V. P., Sales, C. F., Ribeiro, Y. M., Sobjak, T. M., Bazzoli, N., Melo, R. M. C., Elizete Rizzo, E. (2024). Metal-contaminated sediment toxicity in a highly impacted Neotropical river: Insights from zebraffsh embryo toxicity assays. Chemosphere 362, 142627. https://doi.org/10.1016/j.chemosphere.2024.142627.\u003c/li\u003e\n\u003cli\u003eAudry, S., Grosbois, C., Bril, H., Sch\u0026auml;fer, J., Kierczak, J., Blanc, G. (2010). Post-depositional redistribution of trace metals in reservoir sediments of a mining/smelting-impacted wateFShed (the Lot River, SW France). Appl. Gepchem. 25, 778-794. https: // doi: 10. 1016/j. envpol. 2004. 05. 025.\u003c/li\u003e\n\u003cli\u003eBing, H., Zhou, J., Wu, Y., Wang, X., Sun, H., Li, R. (2016). Current state, sources, and potential risk of heavy metals in sediments of Three Gorges Reservoir, China. Environ. Pollut. 214, 485-496. http: //dx. doi. org/10. 1016/j. envpol. 2016. 04. 062.\u003c/li\u003e\n\u003cli\u003eBbek, O., Grygar, T M., Faměra, M. (2015). Geochemical background in polluted river sediments: How to separate the effects of sediment provenance and grain size with statistical rigour? Catena, 135: 240 - 253. https:// doi: 10. 1016 /j. catena. 2015. 07.003.\u003c/li\u003e\n\u003cli\u003eChai, L., Li, H., Yang, Z., Min, X., Liao, Q., Liu, Y., Men, S., Yan, Y., Xu, J. (2016). Heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan, China: distribution, contamination, and ecological risk assessment. Environ. Sci. Pollut. Res. 24, 874-885.https: //doi. org/10.1007/s11356\u0026ndash;016\u0026ndash;7872\u0026ndash;x.\u003c/li\u003e\n\u003cli\u003eChen, D., Peng, B., Feng, X., Wu, S., Liu, J., Zhao, Y., Dai, Y. (2021). Geochemistry of major elements in bed sediments from inlets of the Four RiveFS to Dongting Lake, China. Quaternary Sci. 41(5), 1267-1280 (in Chinese with an English abstract). https: //doi. org/10.11928/j.issn.1001-7410.2021.05.04.\u003c/li\u003e\n\u003cli\u003eCieśla, M., Gruca-Rokosz, R. (2024). Fate of heavy metals in ecosystems of dam reservoirs: Transport, distribution and signiffcance of the origin of organic matter. Environ. Pollut. 361, 124811. https://doi.org/10.1016/j.envpol.2024.124811.\u003c/li\u003e\n\u003cli\u003eDarwish, M. A. G., P\u0026ouml;llmann, H. (2018). Secondary dispersion of trace elements in bottom sediments of the High Dam Lake, South Egypt and North Sudan. Arabian Journal of Geosciences 11, 773. https://link.springer.com/article/10.1007/s12517-018-4127-9.\u003c/li\u003e\n\u003cli\u003eDhivert, E., Grosbois, C., Rodrigues, S., Desmet, M. (2015). Influence of fluvial environments on sediment archiving processes and temporal pollutant dynamics (Upper Loire River, France). Sci. Total Environ. 505, 121-136. https://doi.org/10.1016/j.scitotenv.2014.09.082.\u003c/li\u003e\n\u003cli\u003eDue\u0026ntilde;as-Moreno, J., Mora, A., Narvaez-Montoya, C., Mahlknecht, J. (2024). Trace elements and heavy metal(loid)s triggering ecological risks in a heavily polluted river-reservoir system of central Mexico: Probabilistic approaches. Environ. Res. 262, 119937. https://doi.org/10.1016/j.envres.2024.119937.\u003c/li\u003e\n\u003cli\u003eFang, X., Peng, B., Wang, X., Song, Z., Zhou, X ., Wang, Q., Qin, Z., Tan, C. (2019). Distribution, contamination and source identification of heavy metals in bed sediments from the lower reaches of the Xiangjiang River in Hunan Province, China. Sci. Total Environ. 689, 557-570. https: // doi.org /10. 1016 / j. scitotenv. 2019. 06. 330.\u003c/li\u003e\n\u003cli\u003eFang, X. H., Peng, B., Song, Z. L., Wu, S. C., Chen, D. T., Zhao, Y. F., Liu, J., Dai, Y. N., Tu, X. L. (2021). Geochemistry of heavy metal-contaminated sediments from the Four River inlets of Dongting lake, China. Environ. Sci. Pollut. Res. 28, 27593-27613. https: //doi. org/10. 1007/s11356\u0026ndash;021\u0026ndash;12635\u0026ndash;0.\u003c/li\u003e\n\u003cli\u003eFang, X., Peng, B., Guo, X., Wu, S., Xie, S., Wu, J., Yang, X., Chen, H., Dai, Y. (2023). Distribution, source and contamination of rare earth elements in sediments from lower reaches of the Xiangjiang River, China. Environ. Pollut., 336, 122384. https: //doi. org/10. 1016/j. envpol. 2023. 122384. \u003c/li\u003e\n\u003cli\u003eFleischmann, S., Scholz, F., Du, J., Scholten, J., Vance, D. (2025). Processes controlling nickel and its isotopes in anoxic sediments of a seasonally hypoxic bay. Geochim. Cosmochim. Acta 391, 1-15. https://doi.org/10.1007/s10653-025-02658-8.\u003c/li\u003e\n\u003cli\u003eFok, L., Peart, M. R., Chen, J. (2013). The influence of geology and land use on the geochemical baselines of the East River basin, China. Catena 101, 212-225. https://doi.org/10.1016/j.catena.2012.09.008.\u003c/li\u003e\n\u003cli\u003eFonseca, R., Pinho, C., Oliveira, M. (2016). The influence of particles recycling on the geochemistry of sediments in a large tropical dam lake in the Amazonian region, Brazil. J. South American Earth Sci. 72, 328-350. https://doi.org/10.1016/j.jsames.2016.09.012.\u003c/li\u003e\n\u003cli\u003eFr\u0026eacute;mion, F., Bordas, F., Mourier, B., Lenain, J. F., Kestens, T., Courtin-Nomade, A., 2016. Influence of dams on sediment continuity: A study case of a natural metallic contamination. Sci. Total Environ. 547, 282-294. http: //dx. doi. org/10. 1016/j. scitotenv. 2016. 01. 023.\u003c/li\u003e\n\u003cli\u003eGao, S., Lu, T., Zhang, B., Zhang, H., Han, W., Zhao, Z. (1999). Structure and chemical compositions of East China upper continental crust. Sci. China (series-D) 42(2), 129-140 (in Chinese with an English abstract).\u003c/li\u003e\n\u003cli\u003eGao, L., Gao, B., Xu, D., Peng, W., Lu, J. (2019). Multiple assessments of trace metals in sediments and their response to the water level fluctuation in the Three Gorges Reservoir, China. Sci. Total Environ. 648, 197-205. https: //doi. org/10. 1016/j. scitotenv. 2018. 08. 112.\u003c/li\u003e\n\u003cli\u003eGromet, L. P., Haskin, L. A., Korotev, R. L., Dymek, R. F. (1984). The \u0026ldquo;North American shale composite\u0026rdquo;: its compilation, major and trace element characteristics. Geochim. Cosmochim. Acta. 48, 2469-2482. https: //doi.10.1016/0016-7037(84)90298-9.\u003c/li\u003e\n\u003cli\u003eGuo, J., Xie, Y., Guan, A., Qi, W., Cao, X., Peng, J., Liu, H., Wu, X., Li, C., Wang, D., Qu, J. (2023). Dam construction reshapes sedimentary pollutant distribution along the Yangtze river by regulating sediment composition. Environ. Pollut. 316, 120659. https: //doi. org/10. 1016/j. envpol. 2022. 120659.\u003c/li\u003e\n\u003cli\u003eGao, Z., Wang, C., Zhang, X., Liu, Y., Zhang, Y., Liu, K., Jia, Y. (2022a). Pollution and Sources of Heavy Metals in Sediments of Changsha Section of Xiangjiang River. Journal of Yantai University(Natural Science and Engineering Edition), 36(1): 120\u0026ndash;126. https: // doi: 10. 13951/j. cnki. 37_1213/n. 211015.\u003c/li\u003e\n\u003cli\u003eGreber, N D., Dauphas, N. (2019). The chemistry of fine-grained terrigenous sediments reveals a chemically evolved Paleoarchean emergedcrust. Geochimica et Cosmochimica Acta, 255: 247-264. https: //doi: 10.1016/j.gca.2019.04.012.\u003c/li\u003e\n\u003cli\u003eHu, F., Huang, H., Liu, Z., Hou, Y. (2020). Analysis and evaluation of heavy metal residues in fishes in the Changsha section of Xiangjiang River after impoundment of Xiangjiang Comprehensive Hub Project. Mining and Metallurgical Engineering 40(4), 114-119 (in Chinese with an English abstract). https: //doi:CNKI:SUN:KYGC.0.2020-04-030.\u003c/li\u003e\n\u003cli\u003eJafarabadi, A., Bakhtiyari, A R., Toosi A S. (2017). Spatial distribution, ecological and health risk assessment of heavy metals in marine surface sediments and coastal seawaters of fringing coral reefs of the Persian Gulf, Iran.Chemosphere,185:1090\u0026ndash;1111. https://doi.org/10.1016/j.chemosphere.2017.07.110.\u003c/li\u003e\n\u003cli\u003eKankılı\u0026ccedil;, G. B., T\u0026uuml;z\u0026uuml;n, İ., Kadıoğlu, Y. K. (2013). Assessment of heavy metal levels in sediment samples of Kapulukaya Dam Lake (Kirikkale) and lower catchment area. Environ. Monit. Assess. 185, 6739-6750. https://link.springer.com/article/10.1007/s10661-013-3061-2.\u003c/li\u003e\n\u003cli\u003eLi, X., Bing, J., Zhang, J., Guo, L., Deng, Z., Wang, D., Liu, L. (2022). Ecological risk assessment and sources identification of heavy metals in surface sediments of a river\u0026ndash;reservoir system. Sci. Total Environ. 842, 156683. https://doi.org/10.1016/j.scitotenv.2022.156683.\u003c/li\u003e\n\u003cli\u003eLin, L., Li, C., Yang, W., Zhao, L., Liu, M., Li, Q., Crittenden, J. C. (2020). Spatial variations and periodic changes in heavy metals in surface water and sediments of the Three Gorges Reservoir, China. Chemosphere 240, 124837. https://doi.org/10.1016/j.chemosphere.2019.124837.\u003c/li\u003e\n\u003cli\u003eLiu, S., Wu, K., Yao, L., Li, Y., Chen, R., Zhang, L., Wu, Z., Zhou, Q. (2024). Characteristics and correlation analysis of heavy metal distribution in China\u0026rsquo;s freshwater aquaculture pond sediments. Sci. Total Environ. 931, 172909. https://doi.org/10.1016/j.scitotenv.2024.172909.\u003c/li\u003e\n\u003cli\u003eLiu, S., Yu, F., Lang, T., Ji, Y., Fu, Y., Zhang, J., Ge, C. (2023). Spatial distribution of heavy metal contaminants: The effects of water-sediment regulation in the Henan section of the Yellow River. Sci. Total Environ. 892, 164568. https://doi.org/10.1016/j.scitotenv.2023.164568.\u003c/li\u003e\n\u003cli\u003eLv, J. (2019). Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. Environ. Pollut. 244, 72\u0026ndash;83. https://doi.org/10. 1016/j. envpol. 2018. 09. 147. \u003c/li\u003e\n\u003cli\u003eMclennan, S M., Hemming, S R., Mcdaniel, D K. (1993). Geochemical approaches to sedimentation, provenance, and tectonics. Geological Society of America Special Papers, 284: 21-40. https: //doi: 10. 1130 /SPE284-p21．\u003c/li\u003e\n\u003cli\u003eLiu, X. Y., Wu, Y. F., D. Gregory, D. D., Evans, K., Williams-Jones A. E., Zhang, W., Zhou, M. F., Li, J. W. (2025). Remobilization of Ni in framboidal pyrite within sulfide nodules in Early Cambrian nickeliferous black shales. Chem. Geol. 691, 122913. https://doi.org/10.1016/ j.chemgeo.2025.122913.\u003c/li\u003e\n\u003cli\u003eNesbitt, H. W., Young, G. M. (1982). Early Proterozoic climates and plate motions inferred from major element chemistry of lutites. Nature 299, 715-717. https: //doi:10.1038/299715a0.\u003c/li\u003e\n\u003cli\u003eNguyen, T. M. Y., Vanreusel, A., Mevenkamp, L., Laforce, B., Lins, L., Thanh, T. T., Van, D. N., Xuan, Q. N. (2022). The effect of a dam on the copper accumulation in estuarine sediment and associated nematodes in a Mekong estuary. Environ. Monit. Assess. 194 (Suppl 2), 772. https://link.springer.com/article/10.1007/s10661-022-10183-9.\u003c/li\u003e\n\u003cli\u003ePacheco, F. A. L., Valle Junior, R. F. V., Silva, M. M. A. P., Pissarra, T. C. T., Rolim , G. de S., Melo, M. C., Valera, C. A., Moura, J., Fernandes, L. F. S. (2023). Geochemistry and contamination of sediments and water in rivers affected by the rupture of tailings dams (Brumadinho, Brazil). Appl. Geochem. 152, 105644. https://10.1016/j.apgeochem.2023.105644.\u003c/li\u003e\n\u003cli\u003ePeluso, L., Bulus Rossini, G., Salibi\u0026aacute;n, A., Ronco, A. (2013). Physicochemical and ecotoxicological based assessment of bottom sediments from the Luj\u0026aacute;n River basin, Buenos Aires, Argentina. Environ. Monit. Assess. 185, 5993\u0026ndash;6002. https://link.springer.com/article/10.1007/s10661-012-3000-7.\u003c/li\u003e\n\u003cli\u003ePeng, B., Tang, X., Yu, C., Tan, C., Yin, C., Yang, G., Liu, Q., Yang, K., Tu, X. (2011). Geochemistry of trace metals and Pb isotopes of sediments from the lowermost Xiangjiang River, Hunan Province (P. R. China): implications on sources of trace metals. Environ. Earth Sci. 64 (5), 1455\u0026ndash;1473. https: //doi. org/10. 3724/SP. J. 1011. 2011. 00181.\u003c/li\u003e\n\u003cli\u003ePeng, B., Albert, J., Fang, X., Jiang, C., Wu, S., Li, X., Xie, S., Dai, Y. (2022a). Lead isotopic fingerprinting as a tracer to identify the sources of heavy metals in sediments from the Four Rivers\u0026rsquo; inlets to Dongting Lake, China. Catena 219, 106594. https://doi.org/10.1016/j.catena.2022.106594.\u003c/li\u003e\n\u003cli\u003ePeng, B., Chen, H. S., Fang, X. H., Xie, S. R., Wu, S. C., Jiang, C. X., Dai, Y. N. (2022b). Distribution of Pb isotopes in different chemical fractions in bed sediments from lower reaches of the Xiangjiang River, Hunan province of China. Sci. Total Environ. 829, 154394. https: //dx. doi. org/10. 1016/j. scitotenv. 2022. 154394.\u003c/li\u003e\n\u003cli\u003ePeng, Y., Yu, G. (2024). Assessment of heavy metal pollution on agricultural land in Chengdu city under different anthropogenic pressures based on APCS \u0026ndash; MLR modelling. Ecological Indicators, 165, 112183. https: //doi.org/10.1016/j.ecolind.2024.112183.\u003c/li\u003e\n\u003cli\u003ePerkins, R. B., Mason, C. E. (2015). The relative mobility of trace elements from short-term weathering of a black shale. Appl. Geochem. 56, 67-79. https: //10.1016/j.apgeochem.2015.01.014.\u003c/li\u003e\n\u003cli\u003eRoddaz, M., Viers, J., Brusset, S., Baby, P., Boucayrand, C., H\u0026eacute;rail, G. (2006). Controls in weathering and provenance in the Amazonian foreland basin: insights from major and trace element geochemistry of Neogene Amazonian sediments. Chem. Geol. 226, 31-65. https: //10.1016/j.chemgeo.2005.08.010.\u003c/li\u003e\n\u003cli\u003eSang, C., Zheng, Y., Zhou, Q., Li, D., Liang, G., Gao, Y. (2019). Effects of water impoundment and water-level manipulation on the bioaccumulation pattern, trophic transfer and health risk of heavy metals in the food web of Three Gorges Reservoir (China). Chemosphere 232, 403-414. https://doi.org/10.1016/j.chemosphere.2019.04.216.\u003c/li\u003e\n\u003cli\u003eSreelesh , G. V., Asha Rani, K., Sreelash,\u0026middot;K. (2025). Seasonal dynamics, sources, and health risks of trace and heavy metals in the tropical critical zone of the Western Ghats, India. Environ Geochem Health,47:349. https://doi.org/10.1007/s10653-025-02658-8.\u003c/li\u003e\n\u003cli\u003eSharma, A., Sensarma, S., Kumar, K., Khanna, P. P., Saini, N. K. (2013). Mineralogy and geochemistry of the Mahi River sediments in tectonically active western India: implications for Deccan large igneous province source, weathering and mobility of elements in a semi-arid climate. Geochim. Cosmochim. Acta 104, 63-83. https: // doi: 10.1016/j. gca.2012.11.004.\u003c/li\u003e\n\u003cli\u003eSingh, P. (2009). Geochemistry and provenance of stream sediments of the Ganga River and its major tributaries in the Himalayan region, India. Chem. Geol. 269, 220-236. https: /10.1016/j.chemgeo.2009.09.020.\u003c/li\u003e\n\u003cli\u003eSun, W., Sang, L., Jiang, B. (2012). Trace metals in sediments and aquatic plants from the Xiangjiang River, China. J. Soils Sediments 12, 1649-1657. https: //doi. org/10. 1007/s11368\u0026ndash;012\u0026ndash;0596\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eUSEPA. (2014). Framework for human health risk assessment to inform decision making. US Environmental Protection Agency Washington, DC. Retrieved May 7, 2025, from https://www.epa.gov/sites/default/files/2014-12/documents/hhra-framework-final-2014.pd.\u003c/li\u003e\n\u003cli\u003eVergilio, C. S., Lacerda, D., Souza, T. S., de Oliveira, B. C. V., Fioresi, V. S., de Souza, V. V., Rodrigues, G. R., de Araujo Moreira Barbosa, M. K., Sartori, E., Rangel, T. P., de Almeida, D. Q. R., de Almeida, M. G., Thompson, F., Rezende, E. (2021). Immediate and long-term impacts of one of the woFSt mining tailing dam failure worldwide (Bento Rodrigues, Minas Gerais, Brazil). Sci. Total Environ. 756, 143697. https://doi.org/10.1016/j.scitotenv.2020.143697.\u003c/li\u003e\n\u003cli\u003eVukovic, D., Vukovic, Z., Stankovic, S. (2014). The impact of the Danube Iron Gate Dam on heavy metal storage and sediment flux within the reservoir. Catena 113, 18-23. https://doi.org/10.1016/j.catena.2013.07.012.\u003c/li\u003e\n\u003cli\u003eWang, H., Yuan, W., Zeng, Y., Liang, D., Deng, Y., Zhang, X., Li, Y. (2022). How does Three Gorges Dam regulate heavy metal footprints in the largest freshwater lake of China. Environ. Pollut. 292, 118313. https://doi.org/10.1016/j.envpol.2021.118313.\u003c/li\u003e\n\u003cli\u003eWu, S., Peng, B., Fang, X., Xie, S., Li, X., Jiang, C., Dai, Y. (2021). Distribution and assessment of cadmium contamination in sediments from the Four River inlets to Dongting Lake, China. Environ. Sci. Pollut. Res. 28: 66072-66085. https: //doi. org/10. 1007/s11356\u0026ndash;021\u0026ndash;15636\u0026ndash;1.\u003c/li\u003e\n\u003cli\u003eWu, W., Zheng, H., Xu, S., Yang, J., Liu, W. (2013). Trace element geochemistry of riverbed and suspended sediments in the upper Yangtze River. J. Geochem. Explor. 124, 67-78. https://doi.org/10.1016/j.gexplo.2012.08.005.\u003c/li\u003e\n\u003cli\u003eXu, D., Gao, B., Peng, W., Gao, L., Li, Y. (2019). Geochemical and health risk assessments of antimony (Sb) in sediments of the Three Gorges Reservoir in China. Sci. Total Environ. 660, 1433-1440. https://doi.org/10.1016/j.scitotenv.2019.01.014.\u003c/li\u003e\n\u003cli\u003eYan, M., Gu, T., Chi, Q., Wang, C. (1997). Abundances of elements of China soils and surface geochemical properties of elements. Geophys. Geochem. Explor. 21(3), 16-167 (in Chinese with an English abstract). https://CNKI:SUN:WTYH.0.1997-06-008.\u003c/li\u003e\n\u003cli\u003eYang, H. F., Yang, S. L., Xu, K. H., Millimand , J. D., Wang, H., Yang, Z., Chen, Z., Zhang, C. Y. (2018). Human impacts on sediment in the Yangtze River: A review and new perspectives. Global and Planetary Change 162, 8-17. https://doi.org/10.1016/j.gloplacha.2018.01.001.\u003c/li\u003e\n\u003cli\u003eZhao, Q., Ding, S., Lu,X., Liang, G., Hong, Z., Lu, M., Jing, Y. (2022). Water-sediment regulation scheme of the Xiaolangdi Dam influences redistribution and accumulation of heavy metals in sediments in the middle and lower reaches of the Yellow River. Catena 210, 105880. https://doi.org/10.1016/j.catena.2021.105880.\u003c/li\u003e\n\u003cli\u003eZhao, Y., Peng, B., Fang, X., Wu S., Jing, L., Chen, D., Dai, Y. (2021). Geochemical background of elements in bed sediments from lower reaches of the Xiangjiang River in Hunan Province, China. Geo. Rev. 67(2), 504-522 (in Chinese with an English abstract). https: //doi. 10.16509 /j.Georeview.2021.02.018.\u003c/li\u003e\n\u003cli\u003eZhu, H., Bing, H., Wu, Y., Zhou, J., Sun, H., Wang, J., Wang, X. (2019) . 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. https: //doi. org/10.1016/j. scitotenv. 2019. 02. 016.\u003c/li\u003e\n\u003cli\u003eZhuang, Q., Li, G., Liu, Z. (2018). Distribution, source and pollution level of heavy metals in river sediments from South China. Catena 170, 386-396. https: //doi. org/10. 1016/j. catena. 2018 06. 037.\u003c/li\u003e\n\u003cli\u003eZhao, Z., Li, S., Li, Y. ( 2024). Controlling factors and sources\u0026ndash;specific ecological risks associated with toxic metals in core sediments from cascade reservoirs in Southwest China. Sci. Total Environ. 924, 171570. https: //doi. org/10. 1016/j. scitotenv. 2024. 171570. \u003c/li\u003e\n\u003cli\u003eZhao, Q., Ding, S., Ji, X., Hong, Z., Lu, M., Wang, P. (2021). Relative contribution of the Xiaolangdi Dam to runoff changes in the lower Yellow River. Land 10 (5), 521. https://doi.org/10.3390/land10050521. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Heavy metal contamination, Dam sediment, Mining activity, Ecological risk, human health risk","lastPublishedDoi":"10.21203/rs.3.rs-7302369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7302369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany statistics methods are used to explore heavy metals distribution, contamination, and human health risks of the Xiangjiang dam sediment. Results showed that dam sediments (DS) and pre-dam fluvial sediments(FS) had similar major and some trace element compositions. Meanwhile, the distribution differences of trace metals Ni, Mn, Zn, Cd, Cu and Pb was characterized; for which Ni had distinctly higher (up to 7699.5 mg/kg), and others had lower concentrations in the DS than FS. Heavy metals contamination in DS arrived at high degree for Ni (average I\u003csub\u003eGeo\u003c/sub\u003e of 10.2) and Cd (average I\u003csub\u003eGeo\u003c/sub\u003e of 3.2), and low to moderate for Mn, Zn, Cu, and Pb (1.52\u0026thinsp;\u0026lt;\u0026thinsp;I\u003csub\u003eGeo\u003c/sub\u003e \u0026lt; 3.3). The degree of heavy metal contamination decreased in the DS relative to the FS.\u003c/p\u003e\u003cp\u003eFor adults and children, Metals Ni and Cd may have non carcinogenic risk in some dam sediment (HI\u0026thinsp;\u0026gt;\u0026thinsp;1), and the health risk for child is higher than adult. The non carcinogenic risk of Ni for adult (HI\u0026thinsp;=\u0026thinsp;3.2389) and child (HI\u0026thinsp;=\u0026thinsp;4.9751) are mainly in YP9. The non carcinogenic risk of Cd to adult (HI\u0026thinsp;=\u0026thinsp;1.2579) and child (HI\u0026thinsp;=\u0026thinsp;2.4587) is mainly located in SC1. Source discrimination study showed that metals Mn, Zn, Cd, Cu and Pb in the DS were from mining activities, while Ni was from the waste discharges like agricultural, catering within the reservoir. Protection for metals (especially Ni, Cd) contamination in the dam reservoir should pay a great attention to the anthropogenic activities both in upper river areas and within the dam reservoir.\u003c/p\u003e","manuscriptTitle":"Distribution, contamination and source discrimination of heavy metals in sediments from dam reservoir at Changsha city along the Xiangjiang River, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 06:27:57","doi":"10.21203/rs.3.rs-7302369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T15:13:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T10:25:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T18:59:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281611102102888403958574484411137502368","date":"2025-08-14T08:37:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155656783220007596945265783694297458727","date":"2025-08-11T23:26:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304264537834341212344887473469316369977","date":"2025-08-11T05:56:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307489850527046849070657861157519522590","date":"2025-08-10T15:01:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T14:55:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T21:12:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T17:57:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Geochemistry and Health","date":"2025-08-05T15:29:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8d68c49e-844d-4cfd-9e97-c9e3be473d77","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:08:33+00:00","versionOfRecord":{"articleIdentity":"rs-7302369","link":"https://doi.org/10.1007/s10653-025-02905-y","journal":{"identity":"environmental-geochemistry-and-health","isVorOnly":false,"title":"Environmental Geochemistry and Health"},"publishedOn":"2025-11-26 15:58:43","publishedOnDateReadable":"November 26th, 2025"},"versionCreatedAt":"2025-08-18 06:27:57","video":"","vorDoi":"10.1007/s10653-025-02905-y","vorDoiUrl":"https://doi.org/10.1007/s10653-025-02905-y","workflowStages":[]},"version":"v1","identity":"rs-7302369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7302369","identity":"rs-7302369","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}