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  5. Assessment of the Ecological Risk from Heavy Metals in the Surface Sediment of River Surma, Bangladesh: Coupled Approach of Monte Carlo Simulation and Multi-Component Statistical Analysis

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Article
en
2022

Assessment of the Ecological Risk from Heavy Metals in the Surface Sediment of River Surma, Bangladesh: Coupled Approach of Monte Carlo Simulation and Multi-Component Statistical Analysis

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en
2022
Vol 14 (2)
Vol. 14
DOI: 10.3390/w14020180

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Jesus Simal Gandara
Jesus Simal Gandara

Universidade de Vigo

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Arup Acharjee
Zia Ahmed
Pankaj Kumar
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Abstract

River sediment can be used to measure the pollution level in natural water, as it serves as one of the vital environmental indicators. This study aims to assess heavy metal pollution namely Copper (Cu), Iron (Fe), Manganese (Mn), Zinc (Zn), Nickel (Ni), Lead (Pb), and Cadmium (Cd) in Surma River. Further, it compares potential ecological risk index values using Hakanson Risk Index (RI) and Monte Carlo Simulation (MCS) approach to evaluate the environmental risks caused by these heavy metals. in the study area. With obtained results, enrichment of individual heavy metals in the study area was found in the order of Ni > Pb > Cd > Mn > Cu > Zn. Also, variance in MCS index contributed by studied metals was in the order of Cd > Pb > Ni > Zn > Cu. None of the heavy metals, except Ni, showed moderate contamination of the sediment. Risk index values from RI and MCS provide valuable insights in the contamination profile of the river, indicating the studied river is currently under low ecological risk for the studied heavy metals. This study can be utilized to assess the susceptibility of the river sediment to heavy metal pollution near an urban core, and to have a better understanding of the contamination profile of a river.

How to cite this publication

Arup Acharjee, Zia Ahmed, Pankaj Kumar, Rafiul Alam, M. Safiur Rahman, Jesus Simal Gandara (2022). Assessment of the Ecological Risk from Heavy Metals in the Surface Sediment of River Surma, Bangladesh: Coupled Approach of Monte Carlo Simulation and Multi-Component Statistical Analysis. , 14(2), DOI: https://doi.org/10.3390/w14020180.

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Publication Details

Type

Article

Year

2022

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.3390/w14020180

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