Multimedia environmental chemical partitioning from molecular information

The prospect of assessing the environmental distribution of chemicals directly from their molecular information was analyzed. Multimedia chemical partitioning of 455 chemicals, expressed in dimensionless compartmental mass ratios, was predicted by SimpleBox 3, a Level III Fugacity model, together with the propagation of reported uncertainty for key physicochemical and transport properties, and degradation rates. Chemicals, some registered in priority lists, were selected according to the availability of experimental property data to minimize the influence of predicted information in model development. Chemicals were emitted in air or water in a fixed geographical scenario representing the Netherlands and characterized by five compartments (air, water, sediments, soil and vegetation). Quantitative structure-fate relationship (QSFR) models to predict mass ratios in different compartments were developed with support vector regression algorithms. A set of molecular descriptors, including the molecular weight and 38 counts of molecular constituents were adopted to characterize the chemical space. Out of the 455 chemicals, 375 were used for training and testing the QSFR models, while 80 were excluded from model development and were used as an external validation set. Training and test chemicals were selected and the domain of applicability (DOA) of the QSFRs established by means of self-organizing maps according to structural similarity. Best results were obtained with QSFR models developed for chemicals belonging to either the class [C] and [C; O], or the class with at least one heteroatom different than oxygen in the structure. These two class-specific models, with respectively 146 and 229 chemicals, showed a predictive squared coefficient of q2 ≥ 0.90 both for air and water, which respectively dropped to q2 ∼ 0.70 and 0.40 for outlying chemicals. Prediction errors were of the same order of magnitude as the deviations associated to the uncertainty of the physicochemical and transport properties, and degradation rates. - Research Highlights: →Support vector regression-based QSFRs can be as useful as Multimedia Models →Multimedia chemical partitioning can be predicted directly from molecular information. →Molecular group counts are better input information than molecular descriptors. →Distribution of chemicals is best predicted when considering chemical families