A critically evaluated set of 94 soil water partition coefficients normalized to soil organic carbon content (Koc) is presented for 11 classes of organic chemicals. This data set is used to develop and evaluate (Koc) estimation methods using three
different descriptors. The three types of descriptors used in predicting (Koc) were octanol/water partition coefficient (Kow), molecular connectivity (mχ1), and linear solvation energy relationships (LSERs). The best results
were obtained estimating Koc from Kow, though a slight improvement in the correlation coefficient was obtained by using a two-parameter regression with Kow and the third order difference term from mχ1. Molecular
connectivity correlations seemed to be best suited for use with specific chemical classes. The LSER provided a better fit than mχ1, but not as good as the correlation with Kow. The correlation to predict Koc from Kow
was developed for 72 chemicals; log Koc = 0.903* log Kow + 0.094. This correlation accounts for 91% of the variability in the data for chemicals with log Kow ranging from 1.7 to 7.0. The expression to determine the 95% confidence interval
on the estimated Koc is provided along with an example for two chemicals of different hydrophobicity showing the confidence interval of the retardation factor determined from the estimated Koc. The data showed that Koc is not likely to
be applicable for chemicals with log Kow < 1.7. Finally, the Koc correlation developed using Kow as a descriptor was compared with three nonclass-specific correlations and two "commonly used" class-specific correlations to determine
which methode(s) are most suitable.
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