Multivariate Analysis of Ground Water and Soil Data from a Waste Disposal Site

Environmental site investigations often involve the collection and analysis of hundreds of samples producing data sets that contain thousands of data points, which are difficult and time consuming to analyze. Consequently, investigators often focus on key surrogate parameters for site characterization and remedial action planning and assessment, which results in a large portion of the data collected remaining unused. This study presents the application of principal component analysis (PCA) as an efficient statistical technique to examine large environmental data sets through highlighting patterns in a reduced-variable space. In this work, PCA was applied to ground water and soil data collected from a National Priorities List Superfund site. Analysis of the soil sample data identified several samples with contaminant parameters that were more closely related to those of the waste material than the background samples, and provided both a measure and delineation of the overall soil contamination. Analysis of the ground water data identified elevated metal concentrations due to the corrosion of a carbon steel well screen, a potential hydraulic connection between upper and lower water bearing zones at one well location, and two potentially impacted well locations. These results demonstrate that PCA facilitates the efficient analysis of large environmental data sets, providing a measure of contamination based on multiple sample parameters and aiding in the definition of a remediation boundary. These advantages can expedite data interpretation, guide additional sampling efforts, and define more accurate remediation boundaries, ultimately reducing the total cost of site investigation.