Dimension reduction and source identification for multispecies groundwater contamination.
Assessment of chemical contamination at large industrial complexes with long and sometimes unknown histories of operation represents a challenging environmental problem. The spatial and temporal complexity of the contaminant may be due to changes in production processes, differences in the chemical transport, and the physical heterogeneity of the soil and aquifer materials. Traditional mapping techniques are of limited value for sites where dozens of chemicals with diverse transport characteristics may be scattered over large spatial areas without documentation of disposal histories. In this context, a site with a long and largely undocumented disposal history of shallow groundwater contamination is examined using principal component analysis (PCA). The dominant chemical groups and chemical "modes" at the site were identified. PCA results indicate that five primary and three transition chemical groups can be identified in the space of the first three eigenvectors of the correlation matrix, which account for 61% of the total variance of the data. These groups represent a significant reduction in the dimension of the original data (116 chemicals). It is shown that each group represents a class of chemicals with similar chemo-dynamic properties and/or environmental response. Finally, the groups are mapped back onto the site map to infer delineation of contaminant source areas for each class of compounds. The approach serves as a preliminary step in subsurface characterization, and a data reduction strategy for source identification, subsurface modeling and remediation planning.