A methodology for estimating indoor sources contributing to PM2.5.

Abstract

Quantifying source contributions to indoor PM2.5 levels by indoor PM2.5 sources has been limited by the costs associated with chemical speciation analyses of indoor PM2.5 samples. Here, we propose a new methodology to estimate this contribution. We applied FUzzy SpatioTemporal Apportionment (FUSTA) to a database of indoor and outdoor PM2.5 concentrations in school classrooms plus surface meteorological data to determine the main spatiotemporal patterns (STPs) of PM2.5. We found four dominant STPs in outdoor PM2.5, and we denoted them as regional, overnight mix, traffic, and secondary PM2.5. For indoor PM2.5, we found the same four outdoor STPs plus another STP with a distinctive temporal evolution characteristic of indoor-generated PM2.5. 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 PM2.5 was 5.7 μg m-3, which contributed to 17% of the total PM2.5, and if we consider only school hours, the respective figures are 8.1 μg m-3 and 22%. A cluster-wise indoor-outdoor PM2.5 regression was applied to estimate STP-specific infiltration factors (Finf) per school. The median and interquartile range (IQR) values for Finf 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 PM2.5, including their temporal variability.