Pollution Characteristics and Ecological Risk Assessment of Heavy Metals in Sediments of the Three Gorges Reservoir

Shui, Lujie; Pan, Xunzhang; Chen, Xiaojuan; Chang, Fengyi; Wan, Daqing; Liu, Defu; Hu, Mingming; Li, Shanze

doi:10.3390/w12061798

Cited by 21 publications

(11 citation statements)

References 21 publications

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“…The mean concentrations of Zn and Hg were close to the Yangtze River, and the mean concentrations of Cu, Pb, Cd, and As were lower than that found in sediments of the Yangtze River [30]. Compared with the reports about heavy metals in sediments from the Three Gorges Reservoir, the Cu and Pb were lower, but Zn, Cd, As, and Hg were higher [31]. The Wujiang River is one of the most important input rivers for the Three Gorges Reservoir, it could be a main pollution source of heavy metals in the Three Gorges Reservoir [17].…”

Section: Distribution Of Total Concentrations Of Heavy Metals In Sedimentscontrasting

confidence: 53%

Distribution, Bioavailability and Ecological Risk of Heavy Metals in Surface Sediments from the Wujiang River Basin, Southwest of China

Cai¹,

Zhou²,

Cheng³

et al. 2021

Pol. J. Environ. Stud.

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Chemical speciation of Cu, Zn, Mn, Pb, Cd, As, and Hg in surface sediments from the Wujiang River Basin was analyzed by using modified BCR sequential extraction procedure. The results indicated that the mean total concentrations of all heavy metals except As were higher than their background values. Cu, Zn, Pb, As, and Hg mostly exist in a residual fraction. The Mn and Cd were dominated by the reducible fraction and exchangeable fraction, respectively. According to the risk assessment code (RAC) classification, Cu, Zn, Pb, As, and Hg posed a relatively low risk in this study area, while Mn and Cd posed a high risk in most sampling sites. The I geo classification manifested that Cd has the highest environmental risk and the pollution levels of heavy metals in the surface sediments were generally in the sequence of Cd>Zn>Cu>Pb>Hg>Mn>As. The potential ecological risk index (PERI) was classified as considerable risk in two sampling sites and moderate risk in ten sampling sites. It should pay much attention to the ecological risk of heavy metals from the Wujiang River Basin which will influence the Yangtze River ecosystem eventually.

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Section: Distribution Of Total Concentrations Of Heavy Metals In Sedimentscontrasting

confidence: 53%

Distribution, Bioavailability and Ecological Risk of Heavy Metals in Surface Sediments from the Wujiang River Basin, Southwest of China

Cai¹,

Zhou²,

Cheng³

et al. 2021

Pol. J. Environ. Stud.

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“…The majority of investigators have given their attention to the transport of the trace elements and their distribution in the soil [24,25]. I-geo is useful for assessing and analyzing trace element contents in soil because it intuitively reflects the effects of human activities on trace elements as well as the impact of trace elements on the environment [26]. The PLI represents the number of times the trace element concentrations in the soil surpass the background concentrations, and it provides a summative measure of the total level of trace element toxicity in a given sample.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling

et al. 2021

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The proper assessment of trace element concentrations in the north Nile Delta of Egypt is needed in order to reduce the high levels of toxic elements in contaminated soils. The objectives of this study were to assess the risks of contamination for four trace elements (nickel (Ni), cobalt (Co), chromium (Cr), and boron (B)) in three different layers of the soil using the geoaccumulation index (I-geo) and pollution load index (PLI) supported by GIS, as well as to evaluate the performance of partial least-square regression (PLSR) and multiple linear regression (MLR) in estimating the PLI based on data for the four trace elements in the three different soil layers. The results show a widespread contamination of I-geo Ni, Co, Cr, and B in the three different layers of the soil. The I-geo values varied from 0 to 4.74 for Ni, 0 to 6.56 for Co, 0 to 4.11 for Cr, and 0 to 4.57 for B. According to I-geo classification, the status of Ni, Cr, and B ranged from uncontaminated/moderately contaminated to strongly/extremely contaminated. Co ranged from uncontaminated/moderately contaminated to extremely contaminated. There were no significant differences in the values of I-geo for Ni, Co, Cr, and B in the three different layers of the soil. According to the PLI classification, the majority of the samples were very highly polluted. For example, 4.76% and 95.24% of the samples were unpolluted and very highly polluted, respectively, in the surface layer of the soil profiles. Additionally, 14.29% and 85.71% of the samples were unpolluted and very highly polluted, respectively, in the subsurface layer of the soil profiles. Both calibration (Cal.) and validation (Val.) models of the PLSR and MLR showed the highest performance in predicting the PLI based on data for the four studied trace elements, as an alternative method. The validation (Val.) models performed the best in predicting the PLI, with R2 = 0.89–0.93 in the surface layer, 0.91–0.96 in the subsurface layer, 0.89–0.94 in the lowest layers, and 0.92–0.94 across the three different layers. In conclusion, the integration of the I-geo, PLI, GIS technique, and multivariate models is a valuable and applicable approach for the assessment of the risk of contamination for trace elements, and the PLSR and MLR models could be used through applying chemometric techniques to evaluate the PLI in different layers of the soil.

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“…The higher amount of Cu and Zn in RHB is mainly due to the sedimentations of suspended particles, while that of Cr originated from increased bedrock weathering (Yang et al, 2018). High amounts of Cr were also found in RHB from exogenous sources (Shui et al, 2020;Herath et al, 2022). The elevated levels of Cu, Cr, and Zn in RHB were related to the increased exogenous sources.…”

Section: Dam Impoundment Increased Cr Cu and Zn Accumulationmentioning

confidence: 99%

Dam impoundment elevates soil phosphorus and some trace elements in reservoir hydro‐fluctuation belts

Yang

Xie

2022

Agricultural & Env Letters

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To examine the role of dam impoundment in elevating the levels of soil‐associated phosphorus (P) and trace elements in reservoir hydro‐fluctuation belts (RHB), soil samples in RHB and adjacent uplands (non‐flooded area, NFA) in the Three Gorges Reservoir, China, were collected and analyzed. Concentrations of available P, copper (Cu), chromium (Cr), and zinc (Zn) were found to be higher in RHB than in NFA (p < .05), whereas organic carbon was comparable in RHB and NFA (p > .05). The elevated levels of Cu, Cr, and Zn in RHB were probably associated the repeated drying–rewetting cycles created by the dam impoundment. The 95th percentile of the single‐factor pollution index and geo‐accumulation index in RHB were 1.29 and −0.21 for Zn, 3.21 and 1.15 for Cu and 3.37 and 1.17 for Cr. Elevated pollution potential of soil‐associated Zn, Cu, and Cr existed in RHB of the Three Gorges Reservoir. Core Ideas Dam impoundments decreased total C and total N concentrations in reservoir flooded soils. Cu, Zn, and Cr concentrated in the reservoir flood areas. Variability of Cu, Zn, and Cr was captured in single‐factor pollution index and geo‐accumulation index

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Pollution Characteristics and Ecological Risk Assessment of Heavy Metals in Sediments of the Three Gorges Reservoir

Cited by 21 publications

References 21 publications

Distribution, Bioavailability and Ecological Risk of Heavy Metals in Surface Sediments from the Wujiang River Basin, Southwest of China

Distribution, Bioavailability and Ecological Risk of Heavy Metals in Surface Sediments from the Wujiang River Basin, Southwest of China

Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling

Dam impoundment elevates soil phosphorus and some trace elements in reservoir hydro‐fluctuation belts

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