A spatio-temporally weighted hybrid model to improve estimates of personal PM exposure: Incorporating big data from multiple data sources.

Abstract

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM exposure. Using Shanghai as a case study, the annual average indoor PM concentration was estimated to be 29.3 ± 27.1 μg/m (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM exposure was estimated to be 32.1 ± 13.9 μg/m (n = 365), with indoor PM contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM makes a significant contribution to indoor PM, outdoor PM was responsible for most of the exposure in Shanghai. A heatmap of PM exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.