A methodology for estimating indoor sources contributing to PM2.5

Abstract

Quantifying source contributions to indoor PM_2.5 levels by indoor PM_2.5 sources has been limited by the costs associated with chemical speciation analyses of indoor PM_2.5 samples. Here, we propose a new methodology to estimate this contribution. We applied FUzzy SpatioTemporal Apportionment (FUSTA) to a database of indoor and outdoor PM_2.5 concentrations in school classrooms plus surface meteorological data to determine the main spatiotemporal patterns (STPs) of PM_2.5. We found four dominant STPs in outdoor PM_2.5, and we denoted them as regional, overnight mix, traffic, and secondary PM_2.5. For indoor PM_2.5, we found the same four outdoor STPs plus another STP with a distinctive temporal evolution characteristic of indoor-generated PM_2.5. Concentration peaks were evident for this indoor STP due to children's activities and classroom housekeeping, and there were minimum contributions on sundays when schools were closed. The average indoor-generated estimated contribution to PM_2.5 was 5.7 μg m⁻³, which contributed to 17% of the total PM_2.5, and if we consider only school hours, the respective figures are 8.1 μg m⁻³ and 22%. A cluster-wise indoor–outdoor PM_2.5 regression was applied to estimate STP-specific infiltration factors (F_inf) per school. The median and interquartile range (IQR) values for F_inf are 0.83 [0.7–0.89], 0.76 [0.68–0.84], 0.72 [0.64–0.81], and 0.7 [0.62–0.9], for overnight mix, secondary, traffic, and regional sources, respectively. This cost-effective methodology can identify the indoor-generated contributions to indoor PM_2.5, including their temporal variability.