Statistical Analysis of Chemical Release Rates from Soils

Abstract

Two statistical methods for determining the precision of best-fit model parameters generated from chemical rate of release data are discussed. One method uses the likelihood theory to estimate marginal confidence intervals and joint confidence regions of the release model parameters. The other method uses Monte Carlo simulation to estimate statistical inferences for the release model parameters. Both methods were applied to a set of rate of release data that was generated using a field soil. The results of this evaluation indicate that the precision of F (the fraction of a chemical in a soil that is released quickly) is greater than the precision of k1 (the rate constant describing fast release), which is greater than the precision of k2 (the rate constant describing slow release). This occurs because more data are taken during the time period described by F and k1 than during the time period described by F and k2. In general, estimates of F will be relatively precise when the ratio of k1 to k2 is large, ...