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

ABSTRACT: 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 po

SUBMITTER: Chen B 

PROVIDER: S-EPMC5923615 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature