Standardization of metal concentrations in sediments using regression residuals: an example from a large lake in Florida, USA

A new method of standardizing metal concentrations in sediments was tested on samples from Lake Miccosukee, a large karstic lake in north Florida. Metal concentrations were analyzed in 222 sediment samples from 26 cores representing 9 sampling sites in the lake. Measured sedimentation rates in the lake are low. Percent organic matter strongly increases upward in all the cores. The C/N ratio remains constant throughout all the samples, with a mean value of about 13, regardless of depth or location. All of the geochemical variables are at least approximately log-normally distributed; thus, log-log or semi-log scattergrams were used and the data were log-transformed before statistical calculations were performed. Some elements (Mn, Zn, Hg, Cu, and Ca) are primarily associated with the organic fraction; others (La, Cr, Sr, and Ba) are clearly related to the terrigenous fraction; others show affinities for both fractions. Consequently, no bivariate scattergrams or plots of ratio versus depth – commonly used for standardization by plotting or ratioing a reference element (such as Al) to an element of interest – were found to be adequate for standardization of this dataset. The best method for standardization was found to be one based on multivariate (trivariate) linear regression, using log Al and log C as the independent variables (reference elements representing terrigenous and organic fractions, respectively), and the log of the element of interest as the dependent variable. Residuals (deviations) from the best-fit linear surface were then plotted versus depth in the cores to accomplish the standardization. The results indicate that, with the possible exception of Mn at two sites, there is little evidence of anthropogenic input of trace elements to the lake, and most trace-element concentrations in the lake can be considered as valuable baseline information. A significant finding is that different and erroneous conclusions might have been reached if other standardization methods, not based on trivariate regression, had been employed.