Atmospheric Techniques, Instruments and Modelling: Computational Biophysics of PAH Interactions with the ACE2 Receptor

Traditional atmospheric modelling effectively tracks the dispersion and ambient concentrations of particle-bound pollutants such as Polycyclic Aromatic Hydrocarbons (PAHs), yet it often overlooks the granular, molecular-level mechanisms governing their toxicological impact. This section highlights advanced computational biophysics and molecular docking methodologies as essential “modelling instruments” in contemporary atmospheric science. By simulating the precise chemical interactions between atmospheric PAHs and the Angiotensin-converting enzyme 2 (ACE2) receptor, this research bridges the gap between atmospheric exposure and localized biological response. The methodology establishes a quantitative coupling between macro-scale atmospheric dispersion models and micro-scale computational biophysics. Specifically, spatiotemporal concentration profiles and respiratory tract deposition fractions derived from atmospheric models are transferred as direct input parameters. These macro-scale data determine localised exposure doses and specific PAH congener ratios used for downstream in silico evaluations. Utilizing these environmentally relevant boundary conditions, high-throughput molecular docking simulations identify specific binding affinities and interaction sites between PAH congeners and the ACE2 receptor, focusing on disruptions at catalytic and allosteric regions. Furthermore, Molecular Dynamics (MD) simulations model the thermodynamic stability and dynamic behaviour of these PAH-ACE2 complexes over time. This integrated approach provides significant added value to traditional atmospheric dispersion analysis by translating raw pollutant distribution maps into dynamic toxicological hazard indices. These in silico techniques are critical for predicting how specific, atmospherically modelled PAH doses dysregulate the Renin-Angiotensin System (RAS) and alter cellular susceptibility to viral entry mechanisms. Ultimately, integrating computational biophysics into atmospheric modelling allows researchers to move beyond predicting pollutant distribution to quantitatively understanding the molecular basis of respiratory and cardiovascular pathology, providing the precise evidence required to inform targeted air quality regulations.