Bioaccumulation mechanisms of perfluoroalkyl substances (PFASs) in aquatic environments: Theoretical and experimental insights

Predictive modeling and interpretability analysis of bioconcentration factors for organic chemicals in fish using machine learning

Chemicals are misused and released into the environment, causing adverse effects on people and ecosystems. Assessing the potential environmental risks of these chemicals before their use is crucial. The bioconcentration factor (BCF) is a key parameter used to describe the extent of chemical bioaccumulation. However, previous experiments to determine BCF values are often time-consuming and costly. In this study, a machine learning (ML) model was developed to predict BCF values using molecular descriptors and 9 algorithms. The random forest (RF) model demonstrated strong predictive performance, achieving Rtrain2 and Rtest2 values of 0.949 and 0.935. Moreover, it required only 10 easily obtainable features. The Tanimoto similarity coefficient based on molecular structure was used to characterize the applicability domain (AD). We employed SHAP method, which identified primary factors, including hydrophobicity, molecular volume and shape, polarizability and lipophilicity, that have significantly affected BCF values. Furthermore, partial dependence plots (PDP) and 2D interaction were utilized to delve deeper into the relationship between feature values and model predictions. Results showed that MollogP>4.5, SM1_Dzv>0, SM1_Dzp>0, and ZM1C1>35 were linked to higher lgBCF values (3.2 L/kg), indicating stronger bioconcentration potential. Conversely, under other conditions that suggested weaker bioconcentration capacities, the focus should move to environmental migration. The study provided valuable insights into the factors that influence the bioaccumulation of chemicals, while the RF models can be an effective tool for assessing the bioconcentration potential of chemicals.

Release mechanisms of decabromodiphenyl ether from typical e-waste microplastics into water: Insights from molecular dynamics simulations

E-waste-derived microplastics (MPs) serve as a significant source, have been releasing decabromodiphenyl ether (BDE-209) into aquatic environment. Conventional release kinetics experiments fail to effectively distinguish the three-stage release process, which includes internal diffusion, interfacial mass transfer, and diffusion in the environment. Herein, we took typical flame-retardant plastic (polystyrene, PS) as a paradigm to construct diffusion and release models corresponding to the three-stage release process, with large-scale all-atom molecular dynamics (MD) simulations providing insights into the release process. The level of BDE-209's self-diffusion coefficients (D) was calculated at different release stages: 10-14 (PS matrix), 10-12 (PS-water interface), and 10-10 m2 s-1 (bulk water). BDE-209 exhibits a confined diffusion mode within the PS matrix, significantly diminishing its release capability. At the interface, the strength of dispersion attraction between BDE-209 and the PS surface determines the ease of its release and the partition equilibrium between the two phases. Our findings elucidated the molecular-scale dynamic and thermodynamic mechanisms governing BDE-209 release from MPs into water, expanding the understanding of polybrominated diphenyl ether release from e-waste-derived MPs. Moreover, our established MD simulation methods can be adapted to explore the release or adsorption mechanisms of various additives in different kinds of MPs.

Molecular Mechanisms of Perfluoroalkyl Substances Integration into Phospholipid Membranes

Understanding the molecular interactions of per- and polyfluoroalkyl substances (PFAS) with phospholipids is crucial for elucidating their pathological mechanisms and developing PFAS remediation strategies. In this study, we employ atomistic molecular dynamics simulations to examine PFAS insertion into phospholipid bilayers, including anionic perfluorooctanesulfonic acid (PFOS), perfluorobutanoic acid (PFBA), perfluorooctanoic acid (PFOA), and perfluorododecanoic acid (PFDoA), as well as neutral polytetrafluoroethylene (PTFE). Our study shows that PFAS insertion into lipid bilayers is driven by the free energy gradient between bulk water and the lipid membrane. Positively charged trimethylammonium groups of phospholipids attract negatively charged PFAS, overcoming the surface hydration barrier. Hydrophobic interactions between PFAS fluoroalkyl tails and lipid chains generate a significant driving force for PFAS reorientation and insertion. The increase in electrostatic potential across the lipid surface aids anionic PFAS insertion, but their dehydration hinders further movement. PFAS insertion enhances membrane ordering and decreases lipid fluidity, potentially affecting cellular functions by modifying membrane rigidity. The extended chain length of PFAS facilitates its interactions with the lipid membrane, resulting in a more pronounced influence on altering its structural and dynamic properties.