Chemometrics Modelling of Environmental Data Sets

Abstract

Environmental monitoring studies produce huge amounts of concentration values of chemicals spread at distant geographical sites and during different time periods. Moreover, the content of chemicals is also estimated at different environmental compartments (i.e. air, water, sediments, biota...). All these data values are difficult to cope and evaluate in a simple and fast way using simple univariate statistical tools, specially due to their large number and to their multivariate correlation. In order to discover relevant patterns within large multivariate data sets, the application of modern chemometric methods based in statistical multivariate data analysis and in Factor Analysis is proposed. The basic assumption of chemometric methods is that each of the measured parameter in a particular sample is affected by contributions coming from multiple independent sources. Each one of these sources is characterized by a particular chemical composition and is distributed among samples in an unknown way. After applyin chemometric methods, point and diffuse sources of contaminants in the environment and their origin (natural, anthropogenic, industrial, agricultural...) are identified and their relative distribution among samples (geographical, temporal, among environmental compartments) evaluated. At each sampling site, relative source quantitative apportionment is estimated allowing a global evaluation of the environmental impact, distribution and evolution of main chemical contamination sources in the environment. In this presentation, different chemometric methods will be tested on a series of environmental data sets. In particular, the application of principal component analysis and multivariate resolution methods is shown to be a powerful tool for the goal of chemometrics modelling of contamination sources in large environmental data sets acquired in monitoring studies.