Association, Correlation, and Causation Among Transport Variables of PM2.5

Abstract

The issue of PM2.5 pollution has received significant attention in the literature as it has social, economic, and political implications. Big data sets have been collected by pollution monitoring stations (i.e., nodes) throughout the world, and this has made it possible to quantitatively characterize the dependence of PM2.5 pollution in different regions. Here we divide the dependency relationship into three types: association, correlation, and causation. This study conducted such relationships using three approaches: the random matrix theory (RMT), cross-correlation, and convergent cross-mapping (CCM). The aim of this study is to determine the above three relationships between pollution data from different nodes. A random matrix analysis revealed that pollutant time series are not completely random, but are associated. Further analysis showed that PM2.5 sequences had clear short-range correlations, yet the long-range correlations were blurred. Moreover, at the collect level, there were no clear causalities among pollutant concentrations from different geographical regions, regardless of distance and direction. These results indicate that the dependence of PM2.5 pollution between different sites is complex. Nonetheless, this comprehensive analysis based on big data provided insights into critical issues of general interest, including pollution-induced climate change, and pollution abatement.