Using Proper Regression Methods for Fitting the Langmuir Model to Sorption Data.

One of the most commonly used models for describing contaminant sorption to soils is the Langmuir model. Because the Langmuir model is nonlinear, fitting the model to sorption data requires that the model be solved iteratively using an optimization program. To avoid the use of optimization programs, a linearized version of the Langmuir model is often used so that model parameters can be obtained by linear regression. Although the linear and nonlinear Langmuir equations are mathematically equivalent, there are several limitations to using linearized Langmuir equations. In this study, limitations of using linearized Langmuir equations are examined and alternative approaches presented. Moreover, emphasis is placed on incorporating estimates of data uncertainty into the regression analysis to obtain improved estimates of sorption parameters and their uncertainties. Finally, it will be shown how the use of properly weighted regression data can aid in identifying the most appropriate model for describing sorption data.