Quantifying Individual PM2.5 Exposure with Human Mobility Inferred from Mobile Phone Data

Abstract

Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM2.5 exposure by combining residents’ mobility traces and PM2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM2.5 concentration, we quantify individual PM2.5 exposure and find that the average PM2.5 exposure of residences in Shanghai is 60.37 ug·h·m−3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM2.5 exposure in Shanghai. Our PM2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development.