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

Abstract

Extremely high fine particulate matter (PM_2.5) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM_2.5 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 PM_2.5 in China by integrating mobile-phone locating-request (MPL) big data and station-based PM_2.5 observations. Nationwide experiments show that the proposed method can yield the estimation of population exposure to PM_2.5 concentrations and cumulative inhaled PM_2.5 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 PM_2.5 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.

Keywords: air pollution exposure; dynamic assessment; human mobility; mobile phone data.

Figure 1

Spatial distribution of nationwide monitoring stations for PM_2.5 concentrations (red dots) and meteorological stations (black triangles) in China.

Figure 2

Different facets of population exposure to PM_2.5. (a) Map of population distribution in China on 1 March 2016 (11:00 a.m.). (b) Map of PM_2.5 concentration levels in China on 1 March 2016 (11:00 a.m.). (c) Map of cumulative inhaled PM_2.5 masses in China based on the MPL data on 1 March 2016. (d) Map of cumulative inhaled PM_2.5 in China based on the census data on 1 March 2016. (e–h) show the insets from (a–d) for part of the Northern China.

Figure 3

The estimated population-weighted PM_2.5 concentrations (a) and cumulative inhaled PM_2.5 masses (b) for 359 cities in China with every 3 h from 1 March to 31 March 2016. Note that the x axis represents the time from the first 3-h (2:00 a.m. 1 March 2016) to the last 3-h (23:00 p.m. 31 March 2016), and y axis represents the order of 359 cities.

Figure 4

The biases of cumulative inhaled PM_2.5 mass (a) and the per capita PM_2.5 exposure concentration (b) between the MPL-based estimations and the census-based estimations in China’s cities across different temporal scales.