Calculating the Partition Coefficients of Organic Solvents in Octanol/Water and Octanol/Air.

Partition coefficients define how a solute is distributed between two immiscible phases at equilibrium. The experimental estimation of partition coefficients in a complex system can be an expensive, difficult, and time-consuming process. Here a computational strategy to predict the distributions of a set of solutes in two relevant phase equilibria is presented. The octanol/water and octanol/air partition coefficients are predicted for a group of polar solvents using density functional theory (DFT) calculations in combination with a solvation model based on density (SMD) and are in excellent agreement with experimental data. Thus, the use of quantum-chemical calculations to predict partition coefficients from free energies should be a valuable alternative for unknown solvents. The obtained results indicate that the SMD continuum model in conjunction with any of the three DFT functionals (B3LYP, M06-2X, and M11) agrees with the observed experimental values. The highest correlation to experimental data for the octanol/water partition coefficients was reached by the M11 functional; for the octanol/air partition coefficient, the M06-2X functional yielded the best performance. To the best of our knowledge, this is the first computational approach for the prediction of octanol/air partition coefficients by DFT calculations, which has remarkable accuracy and precision.

Chemometric Assessment of Soil Pollution and Pollution Source Apportionment for an Industrially Impacted Region around a Non-Ferrous Metal Smelter in Bulgaria.

The present study deals with the assessment of pollution caused by a large industrial facility using multivariate statistical methods. The primary goal is to classify specific pollution sources and to apportion their involvement in the formation of the total concentration of the chemical parameters being monitored. This aim is accomplished by intelligent data analysis based on cluster analysis, principal component analysis and principal component regression analysis. Five latent factors are found to explain over 80% of the total variance of the system being conditionally named "organic", "non-ferrous smelter", "acidic", "secondary anthropogenic contribution" and "natural" factor. The apportionment models designate the contribution of the identified sources quantitatively and help in the interpretation of risk assessment and management actions. Since the study takes into account pollution uptake from soil to a cabbage plant, the data interpretation could help in introducing biomonitoring aspects of the assessment. The chemometric expertise helps in revealing hidden relationships between the objects and the variables involved to achieve a better understanding of specific pollution events in the soil of a severely industrially impacted region.

Chemometrics Expertise in the Links Between Ecotoxicity and Physicochemical Features of Silver Nanoparticles: Environmental Aspects.

Studies of the ecotoxicological aspects of nanomaterials in aquatic environments are scarce. Given the growing variety of nanoparticles (NPs), along with the diversity of aquatic species and environments, the key to promoting sound risk assessment in nanoecotoxicology is understanding the mechanisms that govern the fate of NPs in aquatic environments and their behavior at the NP-biota interface. In this paper, data collected from the literature on ecotoxicological effects observed in aquatic species is discussed and analyzed using multivariate statistics techniques. We expand the knowledge of the environmental impact of silver NPs (AgNPs) by testing the acute toxicity of 47 AgNPs on crustacean eukaryotic organisms (Daphnia magna, Thamnocephalus platyurus, and D. galeata). Physicochemical properties, stabilization agents, toxicological end points, and test media were monitored as adding-outcome factors for the evaluation of environmental effects due to exposure to NPs. The chemometrics expertise performed by the use of hierarchical and nonhierarchical cluster analysis and principal component analysis revealed specific links between the ecotoxicology and the physicochemical features of NPs and helped in creating specific patterns of NPs discriminated by ecotoxicity levels and physicochemical parameters.