Enrichment factor and geo-accumulation index of trace metals in sediments of the ship breaking area of Sitakund Upazilla (Bhatiary–Kumira), Chittagong, Bangladesh

Hasan, Asma Binta; Kabir, Sohail; Reza, A.; Zaman, Mohammad Nazim; Ahsan, Aminul; Rashid, M. M.

doi:10.1016/j.gexplo.2012.12.002

Cited by 163 publications

(74 citation statements)

References 31 publications

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“…Manganese is an element of low toxicity having considerable biological importance. It is one of the biogeochemically active transition metals in the aquatic environment (Evans et al 1977;Hasan et al 2013). On the other hand, Fe is often used as an indication of natural changes in the heavy metal carrying capacity of soils and sediments (Rule 1986), and its concentrations were related to the abundance of metal reactive compounds not significantly affected by human activities (Luoma 1990).…”

Section: Discussionmentioning

confidence: 99%

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

2018

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With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu-Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of $ 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenic source. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.

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Section: Discussionmentioning

confidence: 99%

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

2018

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“…The geoaccumulation index ( I geo ) is a common criterion to assess the heavy metal pollution, originally defined by Müller, to evaluate the contamination of metals in sediments . Since the late 1960s, I geo has been widely employed to quantify metal accumulation in contaminated sediments in terrestrial, aquatic, and marine environments , and has been widely applied to the measurement of soil pollution and soil dust in cities . The I geo is calculated by computing the base 2 logarithm of the measured total concentration of the metal over its background concentration using the following mathematical relation:

I_{geo} = {log}_{2} true(C_{normaln} / 1.5 B_{normaln} true)

where C n is the measured concentration of the heavy metal in the sediment and B n is the geochemical background concentration of the metal.…”

Section: Methodsmentioning

confidence: 99%

Distribution and partitioning of heavy metals in sediments of the Xinjiang River in Poyang Lake Region, China

Zhang

Li³

et al. 2014

Env Prog and Sustain Energy

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Total metal concentrations were determined in top and historically subhorizon contaminated sediments collected from four sampling areas (Upstream (UP), Yongping Mining Area Stream (YP), Guixi Mining Area Stream (GX), and Downstream (DM)). The sediments were analyzed for Cu, Zn, Pb, Cd, Cr, As, and Ni using a sequential extraction scheme by Tessier. The results showed that the maximum values of Cu, Zn, and Pb were in top and subhorizon sediments collected from the YP areas, while the DM areas had the highest concentrations of Cd, Cr, As, and Ni. The majority of metal values were higher than the corresponding background values and were increasingly prevalent with the following order: Ni < Cr < Pb < Zn < Cd < As < Cu. Geoaccumulation Index indicated that the contamination degree ranged from uncontaminated to strongly contaminated. The comparison of total metal concentrations in top and subhorizon sediments at depth showed a large difference in the risk to biota. When metal partitioning characteristics were also considered, over 60% of metals were potentially bioavailable and likely toxic. By means of Hierarchical Cluster Analysis, a total number of four groups were distinguished from 16 sampling sites. Sediments from different sampling sites showed an apparent trend for grouping sites, which might be attributed to the various originations of heavy metals and the disturbance from human activities. Factor analysis results revealed two sources of pollutants, which can be explained by two factors: (i) mixed origin or retention phenomena of industrial emissions and (ii) terrigenous. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 713–723, 2015

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“…A value of unity denotes neither enrichment nor depletion relative to the Earth's crust (Hasan et al, 2013). From Table 7 it is evident that Cadmium and nickel has shown no enrichment (EF<1) in all ten sampling locations indicated that these metals were derived from the natural sources, such as underlying geological material.…”

Section: Enrichment Factor (Ef) For the Sedimentmentioning

confidence: 98%

“…To identify the anomalous metal concentration, geochemical normalization of the trace metals data to a conservative element, such as Fe, was employed. Iron was selected because it is a major sorbent phase for trace metals and is a quasi-conservative tracer of the natural metal bearing phases in fluvial and coastal sediments (Hasan et al, 2013). Several authors have successfully used iron to normalize trace metal contaminants (Hasan et al, 2013;Martins et al, 2012;Muñoz-Barbosa et al, 2012).…”

Section: Calculation Of the Enrichment Factor (Ef)mentioning

confidence: 99%

“…This index was introduced by Müller (1981) in order to determine and define metal contamination in sediments by comparing current concentrations with pre-industrial levels. The Geoaccumulation index (I geo ) is used by most of the researchers for trace element studies in soils and sediments (Hasan et al, 2013;Hou et al, 2013;Jumbe and Nandini, 2009;Zahra et al, 2014), is defined in Equation 4:…”

Section: Geo-accumulation Index (I Geo )mentioning

confidence: 99%

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Spatial and Multivariate Analysis of Trace Elements in the Surface Water and Deep Sediments of Fresh Water Aquatic Ecosystem

Arunachalam¹

2014

American Journal of Environmental Sciences

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The aim of this study was to assess the levels of various trace metals present in water and sediment of fresh water aquatic ecosystem during the post monsoon season. The study was extended to identify the trace metal contamination in the water and sediment samples collected along the shores of Lambapur and Peddagattu the tribal villages in India using an Inductively Coupled Plasma Optical Emission Spectrometer (ICPMS). The trace metal contents in water samples were copper-24.2 to 47.5, chromium-4.4 to 8.2, cadmium-0.1 to 0.3, lead-2.1 to 3.8, Nickel-5.9 to 9.7, Zinc-4.6 to 9.7, Manganese-10.8 to 13.2, Iron-52.9 to 157.2 (µg L −1 ) cobalt and arsenic were in BDL and the values were within the limits of Indian drinking water standards (BIS 10500: 1991). The trace metals concentration in the sediment samples ranged from (mg kg −1 ): Copper-61.5 to 113.7, chromium-138.4 to 177.5, cobalt-33.2 to 42.7, cadmium-1.0 to 2.1, lead-57.9 to 103.4, Nickel-36.1 to 56.6, Zinc-51.2 to 102.1, Manganese-610.8 to 1301.7 and Iron-2.5 to 2.9%. In our study, four reliable indices such as Enrichment factor, Contamination factor, Geoaccumulation Index and Pollution Load Index were applied to estimate metal pollution and the results comparison are discussed below. The data generated were used to determine the quality of the sediments based on the enrichment factor, contamination factor and degree of contamination, geochemical index and Pollution Load Index (PLI).

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Enrichment factor and geo-accumulation index of trace metals in sediments of the ship breaking area of Sitakund Upazilla (Bhatiary–Kumira), Chittagong, Bangladesh

Cited by 163 publications

References 31 publications

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

Distribution and partitioning of heavy metals in sediments of the Xinjiang River in Poyang Lake Region, China

Spatial and Multivariate Analysis of Trace Elements in the Surface Water and Deep Sediments of Fresh Water Aquatic Ecosystem

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