Indoor exposure to particulate matter (PM) depends on ventilation-driven transport, yet sensor placement in real rooms is often based on limited point data. This study develops and experimentally validates a transient CFD framework, using RANS airflow coupled with Lagrangian discrete phase tracking, to map PM2.5 and PM10 in a full-scale 2.0 × 3.0 × 2.5 m bedroom with a fixed, non-oscillating pedestal fan and an open window. Airflow was verified by grid independence and validated against 10-point velocity measurements (RMSE = 0.108 m·s−1). Incense experiments (≈31 min burn) provided PM time series over the first 60 min at 16 locations on two heights; emission rate, burning time, and air-change rate (1.96–5.39 ACH) were calibrated so that accepted models achieved aggregate R2 > 0.90. Spatial mapping on a 0.5 m grid shows that PM behavior is governed primarily by airflow-defined accumulation pockets rather than by source proximity alone. A near-source region consistently captured strong early-time peaks, whereas remote low-exchange pockets remained elevated during the decay phase. For PM2.5, the most persistent hotspot is a ceiling-adjacent recirculation pocket, while for PM10, gravitational settling shifted the dominant hotspots toward floor-layer, low-velocity regions. An exposure score combining normalized peak and time-averaged concentrations, interpreted together with particle-track persistence metrics, distinguished transiently traversed regions from true retention pockets. The results show that sensor placement should follow the monitoring objective: near-source regions are more responsive to peak events, ceiling pockets are more suitable for persistent PM2.5 monitoring, and floor hotspots are more critical for PM10. No single fixed sensor location adequately represents both particle sizes in the present bedroom and ventilation configuration.
Estaquio et al. (Sun,) studied this question.