Water quality sample collection, data treatment and results presentation for principal components...

Comprehensive water quality investigations to characterize large watersheds include collection of surface water samples over time at various locations within the watershed and analyses of the samples for multiple chemical and biological constituents. The size and complexity of the resulting dataset make overall evaluations difficult, and as a result, multivariate statistical methods can be useful to evaluate environmental patterns and sources of contamination. The most commonly applied multivariate method in watershed studies is principal components analysis (PCA), which uses correlation among multiple water quality constituents to effectively reduce the number of variables. The reduced set of variables may assist in the identification and description of spatial patterns in water quality that result from hydrologic and geochemical processes and from sources of contamination. The utility of PCA for identifying important environmental factors in a given study is obviously affected by sampling design, constituents analyzed, data quality, data treatment prior to PCA, methods of interpreting PCA results, and other factors. Unfortunately no comprehensive evaluations have been performed and no standard procedures exist for dealing with these issues. This paper examines and evaluates the current state-of-the-science by review of 49 published papers dealing with multivariate (typically PCA) techniques to evaluate watershed water quality. Additionally an example PCA for a surface water quality study in the Illinois River Watershed (IRW) is provided to illustrate methods to address the above issues and to evaluate the sensitivity of results to alternative methods. The example PCA evaluations were consistent with two dominant sources of surface water contamination in the IRW: 1) discharge to the streams from municipal wastewater treatment plants and 2) runoff and infiltration from fields with land applied poultry waste.