LAUSR

Abstract Exposures close to indoor sources are substantially higher than further away. To predict this “proximity effect”, we must determine the human exposure modeling parameter - turbulent diffusion coefficient, and how important room-specific factors such as fan power influence it. We conducted 11 experiments in a residential garage and an office, varying the operating speeds of two household fans at a room corner to create a range of air mixing conditions. In each, SidePak monitors sampled PM2.5 every 10 s over ~2 h at 15–16 points at different horizontal distances and angles from an emitting tracer particle source near the room center. Using an analytical exposure model involving eddy diffusion, the turbulent diffusion coefficient was deduced from the spatial PM2.5 measurements. Values ranged from 0.001 to 0.012 m2/s for power inputs of 0–4 W. By factoring in the room dimensions and air exchange, we found a significant relationship between the turbulent diffusion coefficient and fan power input across the two rooms. The deduced relationship allows assessments of (i) the magnitude of the proximity effect and (ii) the energy budget for reducing exposure via air mixing for a given indoor setting; it can be applied for different air contaminants (e.g., gaseous pollutants) in indoor environments where turbulent mixing dominates over brownian diffusion. For rooms with low air exchange rates, a modest power input from a fan can greatly increase pollutant mixing, reducing proximity exposure with minimal energy consumption.