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

Bin Chen,Bing Xu,Yimeng Song,Tingting Jiang,Ziyue Chen,Bo Huang

doi:10.3390/ijerph15040573

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 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.

Highlights

Air pollutants, especially fine particulate matters such as PM2.5, have been the focus of increasing public concern because of its strong relation with health risks [1,2]
We propose a novel approach to achieve the real-time estimation of population exposure to PM2.5 by integrating mobile-phone locating-request (MPL) big data and station-based PM2.5 observations
In order to better present the experimental results with an entire month in March 2016, at the nationwidethe scale with a nearly real-time updating in this study)demonstrate were we further aggregated pixel-based estimations into 359frequency cities in

Summary

Introduction

Especially fine particulate matters such as PM2.5 (particles with an aerodynamic diameter less than 2.5 μm), have been the focus of increasing public concern because of its strong relation with health risks [1,2]. Numerous epidemiologic studies have established robust associations between long-term exposure to PM2.5 and premature mortality associated with various health conditions—such as heart disease, cardiovascular and respiratory diseases, and lung cancer—that substantially reduce life expectancy [2,3,4,5,6,7]. With the unprecedented economic development and urbanization over the past three decades, the severe and widespread PM2.5 pollution has been one of the biggest health threats in China [8,9]. Res. Public Health 2018, 15, 573; doi:10.3390/ijerph15040573 www.mdpi.com/journal/ijerph

Results

Discussion

Conclusion