Refined Estimates of PM2.5 Exposure and Associated Mortality Burden Using Mobile Phone Big Data

Abstract

Introduction: The improvement of transportation infrastructure, especially the development of high-speed trains, has greatly stimulated the increase of population mobility. Based on census data, existing studies have attempted to quantify the long-term trends of population migration and the consequent environmental impacts. This, however, captured only a small part of population mobility. It ignored the short-term mobility across scales of weeks, days, or even hours, and consequently introduced significant biases in assessing PM2.5 exposure and associated mortality burdens.Methods: In this context, the geographically and time-referenced Call Detail Records (CDRs) makes it possible to address the issues we mentioned above. So in this study, we extracted hourly spatialized population density maps in Jiangsu Province from the CDRs of about 40 million mobile phone users in over 310 thousand macro cells. Based on this Dynamic Population Distribution (DPD), as well as spatiotemporal PM2.5 concentration levels and localized PM2.5 doses-response relationship, we used the Relative Risk Model to evaluate the population-weighted PM2.5 attributed deaths. Finally, Mann-Whitney U Tests were carried out to assess the statistical difference of PM2.5 exposures between DPD and Static Population Distribution (SPD), between daytime and nighttime, and between workdays and weekends.Results and Discussion: The results show that significant difference (p < 0.05) existed between DPD and SPD. In investigating the temporal variation of population-weighted PM2.5 attributed deaths under DPD, significant difference (p < 0.05) between daytime and nighttime can also be found in most of the areas in Jiangsu Province. The difference shows that when more detailed information is included, results from the traditional method may be challenged. The introduction of CDRs data can help us minimize the uncertainties and provide better information for the development of mitigation and adaption strategies.