QSPR models for prediction of the soil sorption coefficient (log KOC) values of 209 polychlorinated trans-azobenzenes (PCt-ABs)

Abstract

The values of the soil sorption coefficient (KOC) have been computed for 209 environmentally relevant trans polychlorinated azobenzenes (PCABs) lacking experimental partitioning data. The quantitative structure–property relationship (QSPR) approach and artificial neural networks (ANN) predictive ability used in models based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6–311++G** basis set and of the semi-empirical quantum chemistry method for property parameterization (PM6) of the molecular orbital package (MOPAC). An experimentally available data on physical and chemical properties of PCDD/Fs and PCBs were used as reference data for the QSPR models and ANNs predictions in this study. Both calculation methods gave similar results in term of absolute log KOC values, while the PM6 model generated in the MOPAC was a much more efficient compared to the DFT model in GAUSSIAN. The estimated values of log KOC varied between 4.93 and 5.62 for mono-, 5.27 and 7.46 for di-, 6.46 and 8.09 for tri-, 6.65 and 9.11 for tetra-, 6.75 and 9.68 for penta-, 6.44 and 10.24 for hexa-, 7.00 and 10.36 for hepta-, 7.09 and 9.82 octa-, 8.94 and 9.71 for nona-Ct-ABs, and 9.26 and 9.34 for deca-Ct-AB. Because of high log KOC values PCt-ABs could be classified as compounds with high affinity to the particles of soil, sediments and organic matter.