Investigating transport of particulate matter from cooking emissions in a multi-story house using low-cost sensor measurements and different modeling approaches

This work investigates the transport of fine particulate matter (PM_2.5) in a multi-story test house using cooking emissions as a point source. The test house was instrumented with 13 PM_2.5 monitors, and the particle sources included pan cooking and air frying, as well as ambient PM_2.5 penetration during periods of no indoor activity. In the absence of indoor sources, we observed about 10 % of ambient PM_2.5 concentrations penetrating indoors with a time lag of ≈ 1 h. Similar peak PM_2.5 concentrations were observed for pan frying and air frying of the same food ingredients. A cross-correlation analysis showed that it took 2–4 min for kitchen peak concentrations to reach other sensors on the first floor and about 8 min to reach the second floor. PM_2.5 concentrations were heterogeneous on the first floor, with non-kitchen areas peaking at 45 % ± 9 % of kitchen levels. Second-floor concentrations were more homogeneous, peaking at 18 % ± 2 % of kitchen levels. Using a typical occupancy scenario, the highest estimated personal PM_2.5 exposure (44 %) was experienced in the kitchen/dining area, which accounted for 9 % of the time spent at home. We used three modeling approaches to analyze particle transport throughout the house, with increasing input requirements: a multi-box model, an empirical model, and the NIST CONTAM model. All models predicted time integrated PM_2.5 concentrations on the 1st and 2nd floors, with R² between 0.57 and 0.82 and RMSE from 6 µg m⁻³ to 11 µg m⁻³.