Using big data from air quality monitors to evaluate indoor PM2.5 exposure in buildings: Case study in Beijing

Due to time- and expense-consuming of conventional indoor PM2.5 (particulate matter with aerodynamic diameter of less than 2.5 mu m) sampling, the sample size in previous studies was generally small, which leaded to high heterogeneity in indoor PM2.5 exposure assessment. Based on 4403 indoor air monitors in Beijing, this study evaluated indoor PM2.5 exposure from 15th March 2016 to 14th March 2017. Indoor PM2.(5) concentration in Beijing was estimated to be 38.6 18.4 mu g/m(3). Specifically, the concentration in non-heating season was 34.9 +/- 15.8 mu g/m(3), which was 24% lower than that in heating season (46.1 +/- 21.2 mu g/m(3)). A significant correlation between indoor and ambient PM2.5 (p < 0.05) was evident with an infiltration factor of 0.21, and the ambient PM2.5 contributed approximately 52% and 42% to indoor PM2.5 for non-heating and heating seasons, respectively. Meanwhile, the mean indoor/outdoor (I/O) ratio was estimated to be 0.73 +/- 0.54. Finally, the adjusted PM2.5 exposure level integrating the indoor and outdoor impact was calculated to be 46.8 27.4 mu g/m(3), which was approximately 42% lower than estimation only relied on ambient PM2.5 concentration. This study is the first attempt to employ big data from commercial air monitors to evaluate indoor PM2.5 exposure and risk in Beijing, which may be instrumental to indoor PM2.5 pollution control. (C) 2018 Elsevier Ltd. All rights reserved.