Temporal and spatial variation of PM2.5 in indoor air monitored by low-cost sensors

Abstract

Indoor air pollution has significant adverse health impacts, but its spatiotemporal variations and source contributions are not well quantified. In this study, we used low-cost sensors to measure PM2.5 concentrations in a typical apartment in Beijing. The measurements were conducted at 15 indoor sites and one outdoor site on 1-minute temporal resolution (convert to 10-minute averages for data analysis) from March 14 to 24, 2020. Based on these highly spatially-and temporally-resolved data, we characterized spatiotemporal variations and source contributions of indoor PM2.5 in this apartment. It was found that indoor particulate matter predominantly originates from outdoor infiltration and cooking emissions with the latter contributing more fine particles. Indoor PM2.5 concentrations were found to be correlated with ambient levels but were generally lower than those outdoors with an average I/O of 0.85. The predominant indoor source was cooking, leading to occasional high spikes. The variations observed in most rooms lagged behind those measured outdoors and in the studied kitchen. Differences between rooms were found to depend on pathway distances from sources. On average, outdoor sources contributed 36% of indoor PM2.5, varying extensively over time and among rooms. From observed PM2.5 concentrations at the indoor sites, source strengths, and pathway distances, a multivariate regression model was developed to predict spatiotemporal variations of PM2.5. The model explains 79% of the observed variation and can be used to dynamically simulate PM2.5 concentrations at any site indoors. The model's simplicity suggests the potential for regional-scale application for indoor air quality modeling.