Toxics

Heavy metal pollution of farmland has emerged as a pressing global environmental challenge, which threatens food security, ecological integrity, and human health [...]

In industrial settings, one of the key environmental challenges is the remediation of soil contaminated by hydrocarbons. Washing the soil with surfactants mobilises and extracts these compounds, making them easier to treat. As it enables the recovery and reuse of soil within sustainable production processes, this technique is part of the circular economy. Soil-washing experiments using surfactants were carried out to determine whether a mixture of anionic and non-ionic surfactants could improve the remediation of soil contaminated by gasoline and diesel fuel compared to the use of a single surfactant. Four surfactants were used (non-ionic: polyoxyethylene lauryl ether and polyoxyethylene (80) sorbitan monooleate; anionic: sodium dodecylbenzenesulfonate and sodium dodecyl sulfate). The aliphatic and aromatic hydrocarbon fractions (C6–C8, C8–C10, C10–C12, C12–C16, C16–C21 and C21–C35) of gasoline and diesel fuel were analysed. Sodium dodecylbenzenesulfonate was selected for the purpose of preparing mixtures with the other two non-ionic surfactants, polyoxyethylene lauryl ether and polyoxyethylene (80) sorbitan monooleate. These surfactant mixtures demonstrated significantly higher removal rates than sodium dodecylbenzenesulfonate alone. Mixtures of sodium dodecylbenzenesulfonate and polyoxyethylene lauryl ether achieved hydrocarbon extraction of between 61% and 68%, while sodium dodecylbenzenesulfonate-polyoxyethylene (80) sorbitan monooleate mixtures obtained extraction of between 58% and 66%. Analysis of the gasoline and diesel hydrocarbon fractions indicated that smaller molecules desorb more easily than larger ones and that aromatics desorb more easily than aliphatics. Furthermore, the mixtures increased the extraction of both aliphatic and aromatic hydrocarbons, particularly the lighter compounds. The variation on removal rates within the hydrocarbon ranges may be related to the octanol–water partition coefficient (K_ow). These improvements with mixtures of anionic and non-ionic surfactants could be exploited to enhance the effectiveness of surfactant-flushing treatments and optimise the design of soil surfactant treatments.

The ubiquity and environmental persistence of per- and polyfluoroalkyl substances (PFASs) have raised significant concerns about their detrimental effects on human health. Collective scientific efforts are increasingly focused on elucidating PFAS toxicity mechanisms and identifying potential low-impact PFAS structures that retain the exceptional properties of this chemical class. To advance the use of in silico methods in PFAS toxicity assessment, we developed a robust modelling framework for predicting PFAS acute oral toxicity class (high or low) in rats, leveraging the enhanced capabilities of the in-house Isalos Analytics Platform. The automated machine learning (autoML) functionality was employed to optimise four ML models—k-nearest neighbours (kNN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and fully connected neural network (NN)—using Mold2 molecular descriptors, and to identify the top-performing model through five-fold cross-validation. The selected kNN model (k = 3) was used for predictions on the held-out testing set, achieving an accuracy of 81.5%, while a Shapley values analysis provided valuable insights into the factors influencing toxicity predictions. Furthermore, the nearest-neighbour-based methodology enabled a read-across structural analysis of PFAS similarity groups consisting of each testing set instance and its three closest neighbours in the training set. This analysis revealed a consistent association between polyaromatic and heterocyclic structural features and high acute oral toxicity. The developed, thoroughly validated read-across model is freely accessible through the INSIGHT RatTox web application as well as the INSIGHT Cheminformatics Platform in Enalos Cloud, supporting high-throughput screening of PFAS compounds and investigation of structural similarities with their nearest neighbours for enriched structural interpretation.