Predicting Partition Coefficients of Short-Chain Chlorinated Paraffin Congeners by COSMO-RS-Trained Fragment Contribution Models.

Environ Sci Technol

Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), Onogawa 16-2, 305-8506 Tsukuba, Ibaraki, Japan.

Published: December 2020

Chlorinated paraffins (CPs) are highly complex mixtures of polychlorinated -alkanes with differing chain lengths and chlorination patterns. Knowledge on physicochemical properties of individual congeners is limited but needed to understand their environmental fate and potential risks. This work used a sophisticated but time-demanding quantum chemically based method COSMO-RS and a fast-running fragment contribution approach to enable prediction of partition coefficients for a large number of short-chain chlorinated paraffin (SCCP) congeners. Fragment contribution models (FCMs) were developed using molecular fragments with a length of up to C in CP molecules as explanatory variables and COSMO-RS-calculated partition coefficients as training data. The resulting FCMs could quickly provide COSMO-RS predictions for octanol-water (), air-water (), and octanol-air () partition coefficients of SCCP congeners with an accuracy of 0.1-0.3 log units root-mean-squared errors. The FCM predictions for agreed with experimental values for individual constitutional isomers within 1 log unit. The distribution of partition coefficients for each SCCP congener group was computed, which successfully reproduced experimental log  ranges of industrial CP mixtures. As an application of the developed FCMs, the predicted and were plotted to evaluate the bioaccumulation potential of each SCCP congener group.

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.est.0c06506DOI Listing

Publication Analysis

Top Keywords

partition coefficients
20
fragment contribution
12
short-chain chlorinated
8
chlorinated paraffin
8
contribution models
8
sccp congeners
8
coefficients sccp
8
sccp congener
8
congener group
8
coefficients
5

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!