Previously, CFD simulations were performed to study the human respiratory system and assist in nasal cavity surgery planning. The simulation pipeline was further augmented with ML methods to automatically segment boundary regions and to improve the quality of nasal cavity surfaces. This study extends previous work, i.e., it includes an external flow region and performs unsteady simulations of particle inhalation with the aim to quantify infection risks. Therefore, the probability of outside particles either entering deep into the nasal cavity, being filtered, or not being inhaled at all is computed. The mesh is comprised of 24·106 cells and the m-AIA simulation framework is used with a combined lattice-Boltzmann and Lagrangian particle tracking approach. Simulations are run on 2,048 CPU cores of JURECA-DC and 60,000 particles are uniformly initialized in a cubic region of 512 (83) cm³ outside the nostrils. The particle diameter is sampled from a uniform discrete distribution of 10⁻⁶, . . . , 10⁻¹ mm. A sinusoidally varying velocity boundary condition is applied at the esophagus boundary for 2.5 s, reaching a maximum inhalation rate of 0.5 l/s at 1.25 s and settling back to 0 at 2.5 s, taking around 160,000 time steps in total. It is observed that large particles of 10−1 mm diameter are successfully filtered by the nasal cavity. Additionally, it is found that all inhaled particles were initialized at most 70 mm away from the nose. This means that keeping the neighborhood of the nose well ventilated should be prioritized over ventilation of the outer space to reduce viral inhalation risks.