A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources

Ben, YuJie; Ma, FuJun; Wang, Hao; Hassan, Muhammad Azher; Yevheniia, Romanenko; Fan, WenHong; Li, Yubiao; Dong, ZhaoMin

A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources

2019

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM₂.₅) 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 PM₂.₅ 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 PM₂.₅ exposure. Using Shanghai as a case study, the annual average indoor PM₂.₅ 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 PM₂.₅ exposure was estimated to be 32.1 ± 13.9 μg/m³ (n = 365), with indoor PM₂.₅ contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM₂.₅ makes a significant contribution to indoor PM₂.₅, outdoor PM₂.₅ was responsible for most of the exposure in Shanghai. A heatmap of PM₂.₅ 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.

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