Correlation and scaling behaviors of $PM_{2.5}$ concentration in China

TL;DR

This study analyzes the seasonal correlation and diffusion patterns of PM2.5 concentrations in China using a network approach, revealing consistent mechanisms across seasons and regional influence patterns.

Contribution

It introduces a network-based method to quantify and compare PM2.5 correlation patterns across seasons and regions in China.

Findings

Correlation distributions can be scaled into a universal function across seasons.

Winter and North China plain show the strongest PM2.5 correlations.

Directional degrees indicate influence pathways along major Chinese regions.

Abstract

Air pollution has become a major issue and caused widespread environmental and health problems. Aerosols or particulate matters are an important component of the atmosphere and can transport under complex meteorological conditions. Based on the data of $PM_{2.5}$ observations, we develop a network approach to study and quantify their spreading and diffusion patterns. We calculate cross-correlation functions of time lag between sites within different season. The probability distribution of correlation changes with season. It is found that the probability distributions in four seasons can be scaled into one scaling function with averages and standard deviations of correlation. This seasonal scaling behavior indicates there is the same mechanism behind correlations of $PM_{2.5}$ concentration in different seasons. Further, from weighted and directional degrees of complex network, different properties of $PM_{2.5}$ concentration are studied. The weighted degrees reveal the strongest correlations of $PM_{2.5}$ concentration in winter and in the North China plain. These directional degrees show net influences of $PM_{2.5}$ along Gobi and inner Mongolia, the North China plain, Central China, and Yangtze River Delta.