Literature DB >> 30815813

Comparison of multi-criteria analysis methodologies for the prioritization of arsenic-contaminated sites in the southwest of China.

Ruihui Chen1, Yanna Xiong2,3, Jiao Li4, Yanguo Teng2, Haiyang Chen2, Jie Yang2.   

Abstract

The issue of contaminated sites has been highlighted as an immediate priority in the 13th Five-Year Plan of China. Identification and prioritization of contaminated sites are of key importance for proposing effective strategies for the regional management of contaminated sites. In this study, three advanced multi-attribute methodologies, the risk-based priority methodology, the regional risk assessment methodology, and the dominance-based rough set approach (DRSA), were comparatively employed to screen contaminated sites in, Guangxi, Southwest of China. The results of the three prioritizations show that the highest ranking site identified by the three methods had great agreement. In regard to the screening attributers, while the risk-based prioritization methodology and regional risk assessment methodology allowed a high discrimination in the screening of contaminated sites associated with different attributes, such as farmland, residential areas, contaminant level, number of people, area, storage quality, site service life, and surrounding communities, the DRSA allowed the identification of contamination strength (CS) and contamination potential (CP).

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Keywords:  Contaminated sites screening; DSRA; Environmental management; Guangxi; Multi-criteria analysis; Priority ranking

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Year:  2019        PMID: 30815813     DOI: 10.1007/s11356-019-04642-z

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Environmental risk mapping of potential abandoned uranium mine contamination on the Navajo Nation, USA, using a GIS-based multi-criteria decision analysis approach.

Authors:  Yan Lin; Joseph Hoover; Daniel Beene; Esther Erdei; Zhuoming Liu
Journal:  Environ Sci Pollut Res Int       Date:  2020-05-28       Impact factor: 4.223

  1 in total

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