Assessment of heavy metal contamination in sediments of the Tigris River (Turkey) using pollution indices and multivariate statistical techniques

Varol, Memet

doi:10.1016/j.jhazmat.2011.08.051

Cited by 836 publications

(346 citation statements)

References 27 publications

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“…Pollution load index (PLI) also provides a simple and comparative mean for assessing the level of heavy metal pollution of the entire sampling area. The empirical index has been determined as the nth root of the product of the nth contamination factor (Cf) (Varol 2011). Sediment pollution index (SPI) is defined as a linear sum of the metal enrichment factors along with the account of metal toxicity weights and is also adopted to assess the sediment quality (Singh et al 2002).…”

Section: Evaluation Of Contamination Degree Of Heavy Metalsmentioning

confidence: 99%

“…I geo (Müller 1969), mC d (Hakanson 1980), PLI (Varol 2011), and developed SPI (Singh et al 2002;Lin et al 2008) in this study were expressed as follows:…”

Section: Evaluation Of Contamination Degree Of Heavy Metalsmentioning

confidence: 99%

“…The contamination levels may be classified descriptively for increasing I geo (Forstner et al 1993) and mC d (Abrahim and Parker 2008) values, and the sediment classification is given for the SPI values (Singh et al 2002) listed in Table 2. When PLI index is more than 1, it means that metal pollution exists; otherwise, there is no pollution (Varol 2011).…”

Section: Evaluation Of Contamination Degree Of Heavy Metalsmentioning

confidence: 99%

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Assessment of heavy metal contamination in the sediments from the Yellow River Wetland National Nature Reserve (the Sanmenxia section), China

Cheng

Wang

Huang

et al. 2015

Environ Sci Pollut Res

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The Yellow River Wetland National Nature Reserve (the Sanmenxia section) is an important area of the Yellow River for two important hydrologic gauging stations: the Sanmenxia reservoir and the Xiaolangdi reservoir. Seven sites along the section were selected: Jiziling, Dinghuwan, Houdi, Canglonghu, Shangcun, Wangguan, and Nancun. After the microwave digestion with aqua regia, concentrations of Cu, Pb, Cd, Cr, Zn, and Mn in the sediments were analyzed by flame atomic absorption spectrometry with air-acetylene flame. The results showed that all the concentrations of Cr detected were from the lithogenic source, and 63 % Mn, 48 % Pb, 41 % Cu, 20 % Cd, and 12 % Zn were from the anthropogenic source. The values of the index of geoaccumulation pointed out that there was moderate contamination of Mn at the Dinghuwan (1.04) and Houdi (1.00) sites (class 2), while the modified degree of contamination denoted that the contamination at the Houdi site (2.02) was moderate, nil to very low at the Nancun and Shangcun sites and low at the other sites, consisting with the tendency of pollution load index. For metal toxicity, the sediment pollution index indicated that the sediments of the Canglonghu site were low polluted, that of the Houdi site is nearly slightly contaminated, and those of others were natural and uncontaminated. It was vital to evaluate the degree of contamination with individual and overall elements and even with the metal toxicity. Cu, Pb, and Mn contaminations were aggravated in the Sanmenxia section, and there maybe was one of the main anthropogenic sources of these metals along the Yellow River. The findings were expected to update the current status of the heavy metal pollution in the Sanmenxia section as well as to create awareness concerning the sound condition of the whole reaches of the Yellow River.

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Section: Evaluation Of Contamination Degree Of Heavy Metalsmentioning

confidence: 99%

“…I geo (Müller 1969), mC d (Hakanson 1980), PLI (Varol 2011), and developed SPI (Singh et al 2002;Lin et al 2008) in this study were expressed as follows:…”

Section: Evaluation Of Contamination Degree Of Heavy Metalsmentioning

confidence: 99%

Section: Evaluation Of Contamination Degree Of Heavy Metalsmentioning

confidence: 99%

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Assessment of heavy metal contamination in the sediments from the Yellow River Wetland National Nature Reserve (the Sanmenxia section), China

Cheng

Wang

Huang

et al. 2015

Environ Sci Pollut Res

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“…Because factor analysis considerably reduces the number of variables and can detect the relationships between the metals, it has been proven to be an effective tool for heavy metal source identification (Han et al 2006;Varol 2011;Panda et al 2010). PCA analysis was applied to help identify heavy metal sources (Tables 4, 5, and 6).…”

Section: Identification Potential Of Natural and Anthropogenic Sourcesmentioning

confidence: 99%

Contamination assessment of arsenic and heavy metals in a typical abandoned estuary wetland—a case study of the Yellow River Delta Natural Reserve

Xie

Sun

Zhang

et al. 2014

Environ Monit Assess

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Coastal and estuarine areas are often polluted by heavy metals that result from industrial production and agricultural activities. In this study, we investigated the concentration trait and vertical pattern of trace elements, such as As, Cd, Ni, Zn, Pb, Cu, and Cr, and the relationship between those trace elements and the soil properties in coastal wetlands using 28 profiles that were surveyed across the Diaokouhe Nature Reserve (DKHNR). The goal of this study is to investigate profile distribution characteristics of heavy metals in different wetland types and their variations with the soil depth to assess heavy metal pollution using pollution indices and to identify the pollution sources using multivariate analysis and sediment quality guidelines. Principal component analysis, cluster analysis, and pollution level indices were applied to evaluate the contamination conditions due to wetland degradation. The findings indicated that the concentration of trace elements decreased with the soil depth, while Cd increases with soil depth. The As concentrations in reed swamps and Suaeda heteroptera surface layers were slightly higher than those in other land use types. All six heavy metals, i.e., Ni, Cu, As, Zn, Cr, and Pb, were strongly associated with PC1 (positive loading) and could reflect the contribution of natural geological sources of metals into the coastal sediments. PC2 is highly associated with Cd and could represent anthropogenic sources of metal pollution. Most of the heavy metals exhibited significant positive correlations with total concentrations; however, no significant correlations were observed between them and the soil salt and soil organic carbon. Soil organic carbon exhibited a positive linear relationship with Cu, Pb, and Zn in the first soil layer (0-20 cm); As, Cr, Cu, Ni, Pb, and Zn in the second layer (20-40 cm); and As, Cr, Cu, Ni, Pb, and Zn in the third layer (40-60 cm). Soil organic carbon exhibited only a negative correlation with Cd (P<0.05), in the second and third layers. As, Cr, Cu, Ni, Pb, and Zn exhibited lower geoaccumulation index values (I geo values), which averaged less than 0 in the three soil layers, this finding indicates that the soils have remained unpolluted by these heavy metals. The mean concentrations of these trace elements were lower than Class I criteria. The degradation wetland restoration suggestions have also been provided in such a way as to restore the reserved flow path of the Yellow River. The results that are associated with trace element contamination would be helpful in providing scientific directions to restore wetlands across the world.

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“…Geochemical studies of sediments in rivers and estuaries have been extended in the last few decades due to the growing awareness of riverine and estuarine pollution and its effects on the ecosystem (Varol 2011;Attia and Ghrefat 2013;Ho et al 2013). Most elements entering the aquatic environment are ultimately fixed to sediments.…”

Section: Introductionmentioning

confidence: 99%

Assessment of trace element contamination in sediment cores from the Pearl River and estuary, South China: geochemical and multivariate analysis approaches

Yang

Liu

Chen

et al. 2014

Environ Monit Assess

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Twenty-four major and trace elements and the mineralogical composition of four sediment cores along the Pearl River and estuary were analyzed using ICP-AES, ICP-MS, and X-ray diffraction (XRD) to evaluate contamination levels. The dominant minerals were quartz, kaolinite, and illite, followed by montmorillonite and feldspars, while small amounts of halite and calcite were also observed in a few samples. Cluster analysis (CA) and principal component analysis (PCA) were performed to identify the element sources. The highest metal concentrations were found at Huangpu, primarily due to wastewater treatment plant discharge and/or the surreptitious dumping of sludge, and these data differed from those of other sources. Excluding the data from Huangpu, the PCA showed that most elements could be considered as lithogenic; few elements are the combination of lithogenic and anthropogenic sources. An antagonistic relationship between the anthropogenic source metals (K, Ba, Zn, Pb, Cd, Ag, Tl, and U) and marine source metals (Na, Mg, Ti, V, and Ca) was observed. The resulting normalized Al enrichment factor (EF) indicated very high or significant pollution of Cd, Ag, Cu, Zn, Mo, and Pb at Huangpu, which may cause serious environmental effects. Conflicting results between the PCA and EF can be attributed to the background values used, indicating that background values must be selected carefully.

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Assessment of heavy metal contamination in sediments of the Tigris River (Turkey) using pollution indices and multivariate statistical techniques

Cited by 836 publications

References 27 publications

Assessment of heavy metal contamination in the sediments from the Yellow River Wetland National Nature Reserve (the Sanmenxia section), China

Assessment of heavy metal contamination in the sediments from the Yellow River Wetland National Nature Reserve (the Sanmenxia section), China

Contamination assessment of arsenic and heavy metals in a typical abandoned estuary wetland—a case study of the Yellow River Delta Natural Reserve

Assessment of trace element contamination in sediment cores from the Pearl River and estuary, South China: geochemical and multivariate analysis approaches

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