Statistical analysis of chemical release rates from soils

Two statistical methods for determining the precision of best-fit model par ameters generated from chemical rate of release data are discussed. One met hod uses the likelihood theory to estimate marginal confidence intervals an d joint confidence regions of the release model parameters. The other metho d uses Monte Carlo simulation to estimate statistical inferences for the re lease model parameters. Both methods were applied to a set of rate of relea se data that was generated using a field soil. The results of this evaluati on indicate that the precision of F (the fraction of a chemical in a soil t hat is released quickly) is greater than the precision of k(1) (the rate co nstant describing fast release), which is greater than the precision of k(2 ) (the rate constant describing slow release). This occurs because more dat a are Taken during the time period described by F and k(1) than during the time period described by F and k(2). In general, estimates of F will be rel atively precise when the ratio of k(1) to k(2) is large, estimates of k(1) for soil/chemical matrices with a high F will be relatively precise, and es timates of k(1) for soil/chemical matrices with a low F will be relatively precise, provided that sufficient time is allowed for full release.