Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data

Extremely high fine particulate matter (PM2.5) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM2.5 exposure assessments have practical limitations, due to the assumption that population distribution or air pollution levels are spatially stationary and temporally constant and people move within regions of generally the same air quality throughout a day or other time periods. To deal with this challenge, we propose a novel method to achieve the real-time estimation of population exposure to PM2.5 in China by integrating mobile-phone locating-request (MPL) big data and station-based PM2.5 observations. Nationwide experiments show that the proposed method can yield the estimation of population exposure to PM2.5 concentrations and cumulative inhaled PM2.5 masses with a 3-h updating frequency. Compared with the census-based method, it introduced the dynamics of population distribution into the exposure estimation, thereby providing an improved way to better assess the population exposure to PM2.5 at different temporal scales. Additionally, the proposed method and dataset can be easily extended to estimate other ambient pollutant exposures such as PM10, O₃, SO₂, and NO₂, and may hold potential utilities in supporting the environmental exposure assessment and related policy-driven environmental actions.