A review of the predictive models estimating association of neutral and ionizable organic chemicals with dissolved organic carbon.

Dissolved organic carbon (DOC) plays a key role in environmental transport, fate and bioavailability of organic chemicals in terrestrial and aquatic ecosystems. Predicting the association of contaminants to DOC is therefore crucial in modelling chemical exposure and risk assessment. The models proposed so far to describe interaction mechanisms between chemicals and DOC and the most influential variables have been reviewed. The single-parameter linear free energy relationships (sp-LFERs) and the poly-parameter linear free energy relationships (pp-LFERs) in the form of linear solvation energy relationships (LSERs) currently available in literature for estimating dissolved organic carbon/water partition (KDOC) and distribution (DDOC) coefficients for organic chemicals were discussed, and limits of the existing approaches explored. For neutral chemicals many predictive equations are currently available in literature, but the quality of the input data on which they are based is often questionable, due to the lack of an unequivocal definition of DOC among different references and to the different and often unreliable KDOC measurement method. For ionizable chemicals instead there is a substantial lack of predictive approaches that need to be fulfilled since just few models are nowadays available to predict DDOC of ionized species. This paper reviews the current approaches for neutral and ionizable chemicals proposing guidelines to select conditions for obtaining reliable data and predictive equations for an improved estimation of KDOC and DDOC.