A linear solvation energy relationship model of organic chemical partitioning to particulate organic carbon in soils and sediments

Abstract

Predicting the association of contaminants with particulate organic matter in the environment is critical in determining the fate and bioavailability of chemicals. A ubiquitous measure of contaminant association with soil and sediment particulate organic matter is the organic carbon partition coefficient K(OC) . Chemical class-specific models relating the K(OC) to the octanol-water partition coefficient K(OW) have been used to predict the partitioning to organic carbon in the water column and sediment for nonpolar hydrophobic pollutants and some polar pollutants. A single linear solvation energy relationship (LSER) is proposed as a simpler and chemically based alternative for predicting K(OC) for a more diverse set of compounds. A chemically diverse set of K(OC) data is used to obtain a more robust and more universally representative model of organic carbon partitioning than previously available LSER models. The resulting model has a root mean square error (RMSE) of prediction for log K(OC) of RMSE = 0.48 for the fitted data set and RMSE = 0.55 for an independent data set. An analysis of LSER coefficients highlights the relative importance of hydrogen bonding interactions.