Journal
ENVIRONMENTAL POLLUTION
Volume 157, Issue 8-9, Pages 2275-2281Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envpol.2009.03.033
Keywords
PCA; Sediment; Metal; Estuary; Data pre-treatment
Categories
Funding
- Queen Mary, University of London
- University of London, Central Research Fund
Ask authors/readers for more resources
Principal components analysis (PCA) is a multivariate statistical technique capable of discerning patterns in large environmental datasets. Although widely used, there is disparity in the literature with respect to data pre-treatment prior to PCA. This research examines the influence of commonly reported data pretreatment methods on PCA outputs, and hence data interpretation, using a typical environmental dataset comprising sediment geochemical data from an estuary in SE England. This study demonstrated that applying the routinely used log (x + 1) transformation skewed the data and masked important trends. Removing outlying samples and correcting for the influence of grain size had the most significant effect on PCA outputs and data interpretation. Reducing the influence of grain size using granulometric normalisation meant that other factors affecting metal variability, including mineralogy, anthropogenic sources and distance along the salinity transect could be identified and interpreted more clearly. (C) 2009 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available