A spatio-temporally weighted hybrid model to improve estimates of personal PM_2.5 exposure: Incorporating big data from multiple data sources

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) 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_2.5 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_2.5 exposure. Using Shanghai as a case study, the annual average indoor PM_2.5 concentration was estimated to be 29.3 ± 27.1 μg/m³ (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM_2.5 exposure was estimated to be 32.1 ± 13.9 μg/m³ (n = 365), with indoor PM_2.5 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_2.5 makes a significant contribution to indoor PM_2.5, outdoor PM_2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM_2.5 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.