Statistical and spatial assessment of soil heavy metal contamination in areas of poorly recorded, complex sources of pollution

Abstract

The assessment of soil heavy metal contamination and the quantification of its sources and spatial extent represent a serious challenge to the environmental scientists and engineers. To date, statistical and spatial analysis tools have been used successfully to assess the amount and spatial distribution of soil contamination. However, these techniques require vast amounts of samples and a good historical record of the study area. Furthermore, they cannot be applied in cases of complex or poorly recorded contamination and provide only a qualitative assessment of the pollution sources. The author has developed a methodology that combines statistical and geostatistical analysis tools with geographic information systems for the quantitative and spatial assessment of contamination sources.

This paper focuses on the techniques that may be employed to explore the structure of a soil data set. Soil contamination data from Lavrio old mine site in Greece were used to illustrate the methodology. Through the research, it was found that principal component and factor analysis tools delineate the principal processes that drive pollution distribution. However, the spatial assessment and quantification of multiple pollution sources cannot be resolved. This aspect is explored in detail in the second paper of the series, focusing on the exploitation of principal component and factor analysis results as inputs for canonical correlation, geostatistical analysis and geographic information systems tools.