Dimension reduction and source identification for multispecies groundwatercontamination

Assessment of chemical contamination at large industrial complexes with lon g and sometimes unknown histories of operation represents a challenging env ironmental 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 dozen s of chemicals with diverse transport characteristics may be scattered over large spatial areas without documentation of disposal histories. In this c ontext, a site with a long and largely undocumented disposal history of sha llow groundwater contamination is examined using principal component analys is (PCA). The dominant chemical groups and chemical "modes" at the site wer e identified. PCA results indicate that five primary and three transition c hemical groups can be identified in the space of the first three eigenvecto rs of the correlation matrix, which account for 61% of the total variance o f the data. These groups represent a significant reduction in the dimension of the original data (116 chemicals). It is shown that each group represen ts a class of chemicals with similar chemo-dynamic properties and/or enviro nmental 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, a nd a data reduction strategy for source identification, subsurface modeling and remediation planning. (C) 2001 Elsevier Science B.V. All rights reserv ed.