Principal Component Analysis as an Outlier Detecti

Authors: Richard J C Brown Andrew S Brown

Publish Date: 2012/03/28

Volume: 223, Issue: 7, Pages: 3807-3816

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Abstract

Principal component analysis has been used as a tool for the detection of potentially outlying observations in multivariate data sets of polycyclic aromatic hydrocarbon concentrations PAHs in ambient air The outlier statistic developed is the vector distance of each observation at a given site from the origin of principal component space It is shown that the success of this technique relies on the usually very strong correlation of concentrations of different PAHs in ambient air such that any deviation from this correlation is noteworthy Indeed it is so strong that the first principal component has been omitted from the technique since it is related mostly to absolute concentration The method has been successful in detecting observations with unusually high concentrations of one or more PAHs Moreover it has been possible to identify periods where the UK pollution climate was abnormal during periods of extreme weather Advice and guidance for the practical use of the technique is also givenThe funding of the National Measurement System’s Chemical and Biological Metrology Programme by the UK Department for Business Innovation and Skills is gratefully acknowledged The UK Department of Environment Food and Rural Affairs’ funding of the UK PAH Monitoring Network is gratefully acknowledged

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