# Temporal and spatial correlation patterns of air pollutants in Chinese cities

## TL;DR

This study analyzes the temporal and spatial correlation patterns of six air pollutants across 350 Chinese cities using network methods, revealing intraday patterns, long-term correlations, and spatial clustering, which improve understanding of pollution dynamics.

## Contribution

It introduces the application of PMFG network analysis to explore temporal and spatial correlations of air pollutants in Chinese cities, highlighting new insights into their evolution and spatial structure.

## Key findings

- Pollutants show strong intraday patterns except O₃.
- All pollutants exhibit long-term correlations.
- O₃ has the highest spatial connectivity.

## Abstract

As a huge threat to the public health, China's air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants' hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O$_3$, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O$_3$ is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM$_{10}$ and O$_3$, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants' evolutionary process, but also shed lights on the application of complex network methods into geographic issues.