Journal
MARINE POLLUTION BULLETIN
Volume 64, Issue 4, Pages 688-698Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.marpolbul.2012.01.032
Keywords
Surface water quality; Kinta River; Chemometrics; Factor analysis; Cluster analysis; Discriminant function analysis
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This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes. (C) 2012 Elsevier Ltd. All rights reserved.
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