A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

Abstract

In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 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 PM2.5 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 NO2 pollutants play a vital role in the PM2.5 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 PM2.5 concentrations has been achieved.

Relationship Analysis of AQI and Air Pollutants with Association Rule

“…With regard to the relationship between AQI and air pollutants and the relationship between air pollutants, many scholars in China have conducted research in recent years using correlation analysis and other measurement methods.In the study of the problem of the relationship between air pollutants, JinZhihao [1] used spss software to conduct correlation analysis and cluster calculation for each air pollutant for the monitoring data of ambient air pollutants in Hongkou District, Shanghai in 2019, and concluded that there is a strong correlation between each pollutant (NO2, PM10, PM2.5 and CO all have significant positive correlations, while O3 has negative correlations with other pollutants.) Considering that the time trend will have some influence on the relationship between each pollutant, Wang Jin [2] introduced the detrended correlation coefficient to analyze the correlation between PM2.5 and other air pollutants in Urumqi to obtain the change of the correlation between air pollutants at different time points and different time scales.…”

Exploring the Change in PM2.5 and Ozone Concentrations Caused by Aerosol–Radiation Interactions and Aerosol–Cloud Interactions and the Relationship with Meteorological Factors

“…Studies have also revealed [12,13] that HO 2 radicals and other precursors are important in the atmosphere, and their uncertainties should be underlined in quantification and numerical simulations. Since NOx can serve as the precursor of ozone pollution and can play a significant role in the level of regional PM 2.5 concentrations [14], the mechanism of the combined pollution on a local scale has triggered further discussions about local aerosol feedback along with a variety of other variables.…”