Environmental fate or transport of pharmaceutical waste depends on the adsorptive interactions of pharmaceuticals with various environmental phases e.g. soil, sediment, microalgae, and bacteria etc. Therefore, it is important to understand these adsorptive interactions. As part of the study, we studied the adsorptive interaction of 30 chemicals with microalgae, i.e. Chlorella vulgaris, because it is ubiquitous and its surface area occupies a high proportion in aquatic environments. For this study, isotherms between C. vulgaris and 30 micropollutants in neutral and ionic forms (i.e. 15 cations, 5 anions, and 10 neutrals) were experimentally measured, and their adsorptive affinities were then theoretically predicted based on the concept of the linear free energy relationship. For modeling, the dataset was divided into a training set and a test set, where the training set was used for model development and the test set was performed for model validation. This process was repeated ten times. Finally, we suggested one model which has high predictability in R2 of 0.96 and standard error (SE) of 0.17 log unit for the training set, R2 of 0.818 and SE = 0.217 log unit for the test set, and R2 of 0.926 and SE of 0.169 log unit for the total dataset. Moreover, it was found that dispersive force, H-bond basicity, molecular volume, and electrostatic interaction of anion significantly contribute to the model developed based on the entire dataset. Here, dispersive and hydrophobic interactions (proportional to the magnitude of molecular size) are main attractive forces, while the rest cases are repulsive. In addition, it was found that the adsorption property of the surface of C. vulgaris differs from those of Gram negative bacteria Escherichia coli and dissolved organic matters in an aquatic environment.
Keywords: Adsorption; Ionic pharmaceuticals; LFER; Micropollutants; Modeling; Prediction; QSAR.
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