A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM(2.5) concentrations in China

In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM(2.5) concentrations in China from 2013 to 2017. NO(2), SO(2), CO and O(3) were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM(2.5) concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO(2) pollutants play a vital role in the PM(2.5) concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM(2.5) concentrations has been achieved.