The application of positive matrix factorization in the analysis, characterisation and detection of contaminated soils

Abstract

Multivariate factor analytical techniques are widely used for the approximation, in terms of a linear combination of factors, of multivariate experimental data. The chemical composition of soil samples are multivariate in nature and provide datasets suitable for the application of these statistical techniques. Recent developments of multivariate factor analytical techniques have led to the approach of Positive Matrix Factorization (PMF), a weighted least squares fit of a data matrix in which the weights are determined depending on the error estimates of each individual data value. This approach relies on more physically significant assumptions than methods like Principal Components Analysis which is frequently used in the analysis of soil datasets. In this paper we apply PMF to characterise the pollutant source in a set of geographically referenced soil samples taken within a 200 m radius of a site characterised by a high concentration of heavy metals. Each sample has been analysed for major and minor elements (using wavelength-dispersive X-ray fluorescence spectrometry), carbon, hydrogen and nitrogen (using a CHN elemental analyzer) and mercury (using cold-vapour atomic absorption spectrometry). Analysis of the soils using PMF resulted in a successful partitioning of variances into sources related to background soil geochemistry, organic influences and those associated with the contamination. Combining these results with a geostatistical approach successfully demarcated the main source of the combined organic and heavy metal contamination.