During the delivery of inhaled medicines, and depending on the size distribution of the particles in the formulation, airway bifurcations are areas of preferential deposition. Previous studies of laminar flow through airway bifurcations point to an interplay of inertial and centrifugal forces that leads to rich flow phenomena and controls particle deposition patterns. However, recent computational studies have shown that the airflow in the upper human airways is turbulent during much of the respiratory cycle. The question of how the presence of turbulence modifies these effects remains open. In this study, we perform for the first time Direct Numerical Simulations (DNS) of fully developed turbulent flow through a single human airway bifurcation model, emulating steady prolonged inspiration and expiration. We use the rich information obtained from the DNS in order to identify key structures in the flow field and scrutinize their role in determining deposition patterns in the bifurcation. We find that the vortical structures present in the bifurcation during expiration differ from those identified during inspiration. While Dean vortices are present in both cases, a set of three dimensional “carinal vortices” are identified only during expiration. A set of laminar simulations in the same geometries, but at lower Reynolds numbers, allow us to identify key differences in aerosol deposition patterns between laminar and turbulent respiration. We also report deposition fractions for representative Stokes numbers for both laminar and turbulent conditions. Given the suspected role of external mechanical stress on the airway epithelium in determining mucus clearance and chronic disease development, here we report wall shear stress distributions for both the turbulent and laminar cases. Finally, we also perform Large Eddy Simulations (LES) and Reynolds-Averaged Navier-Stokes (RANS) simulations for the same configuration in order to asses their performance as compared to DNS. We find that LES and RANS perform well and that they are able to capture the key characteristics of the flow field. The agreement between DNS and RANS holds true only for the mean flow field, which is primarily influenced by curvature effects