Pattern recognition and classification of sediments according to their metal content using chemometric tools. A case study: the estuary of Nerbioi-Ibaizabal River (Bilbao, Basque Country)

Abstract

Chemometrics are increasingly used in environmental monitoring studies, but are still far from being accepted as routine tools by field specialists. The multivariate character of usually highly correlated environmental data recommends the use of advanced chemometrics as part of the analytical methodology in order to get information on the basic structure of data. In this work, we have applied a battery of non-supervised (Principal Component Analysis (PCA)) and supervised (k-Nearest Neighbour (k-NN), Soft Independent Modelling of Class Analogies (SIMCA), Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)) multivariate techniques on a specific environmental dataset. The dataset consists on the concentration of 14 elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn) in 95 sediments collected at eight different locations of the estuary of the Nerbioi-Ibaizabal River (Bilbao, Basque Country) during 12 sampling campaigns conducted every 3 months between 2005 and 2008. The study aims to present a simple methodology of general applicability which may result in a flexible and practical tool to assess chemical pollution in sediments of a given specific site.