Application of fingerprint-based multivariate statistical analyses in source characterization and tracking of contaminated sediment migration in surface water.

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ID: 28704
2013
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Ranked #86 of 145 articles by views in Environmental pollution (Barking, Essex : 1987)

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Abstract
This study investigates the suitability of multivariate techniques, including principal component analysis and discriminant function analysis, for analysing polycyclic aromatic hydrocarbon and heavy metal-contaminated aquatic sediment data. We show that multivariate "fingerprint" analysis of relative abundances of contaminants can characterize a contamination source and distinguish contaminated sediments of interest from background contamination. Thereafter, analysis of the unstandardized concentrations among samples contaminated from the same source can identify migration pathways within a study area that is hydraulically complex and has a long contamination history, without reliance on complex hydrodynamic data and modelling techniques. Together, these methods provide an effective tool for drinking water source monitoring and protection.
Reference Key
chen2013applicationenvironmental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chen, Fei;Taylor, William D;Anderson, William B;Huck, Peter M;
Journal Environmental pollution (Barking, Essex : 1987)
Year 2013
DOI
10.1016/j.envpol.2013.04.028
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