Monte Carlo simulation was used to identify the advantages and disadvantages of different experimental designs for measuring the rate of release (ROR) of chemicals from soils. Specifically, Monte Carlo simulation was used to identify changes to the sampling schedule and number of replicates that may result in greater precision in the best-fit model parameters. The purpose was to identify the factors that should be considered in a rate of release experiment to obtain the desired precision in ROR results. Factors that were investigated include the number of replicates, the scatter of the laboratory data, and the length and distribution of samples in a rate study. The fraction of chemical released quickly, F, was shown to be estimated with high precision for a wide range of experimental designs. The rate constant describing fast release, k 1 , can be estimated with high precision if sufficient samples are taken near the beginning of the rate study. The rate constant describing slow release, k 2 , can be estimated with high precision if sufficient samples are taken near the time corresponding to 1/k 2 . For highly weathered soils containing hydrophobic organic chemicals, a precise estimate of k 2 will require a long laboratory study.

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The modeling of lakes, rivers, and estuaries is a fascinating subject that combines interesting facets of mathematics, statistics, physics, chemistry, and biology. Because of the complexity of natural systems, such modeling is always an approximation of the real world—and sometimes not a very good one. It is for this reason that modeling is not just science but also art. It is also for this reason that there are few good texts offering practical advice on modeling.