Spatial statistics of atmospheric particulate matter in China

TL;DR

This study analyzes the spatial distribution and dynamics of atmospheric particulate matter in China using turbulence methodologies, revealing multifractal and intermittent behaviors on mesoscale ranges.

Contribution

It introduces turbulence-based spatial analysis to characterize particulate matter distribution, highlighting multifractality and intermittency in atmospheric pollution.

Findings

Spatial correlation function follows a log-law with exponent 0.45.

Particulate matter exhibits multifractal and intermittent spatial structures.

Intermittency is more pronounced than passive scalars, influenced by mesoscale atmospheric movements and local sources.

Abstract

In this paper, the spatial dynamics of the atmospheric particulate matters (resp. PM$_{10}$ and PM$_{2.5}$) are studied using turbulence methodologies. It is found experimentally that the spatial correlation function $\rho(r)$ shows a log-law on the mesoscale range, i.e., $50\le r\le 500\,{km}$, with an experimental scaling exponent $\beta=0.45$. The spatial structure function shows a power-law behavior on the mesoscale range $90\le r\le 500\,{km}$. The experimental scaling exponent $\zeta(q)$ is convex, showing that the intermittent correction is relevant in characterizing the spatial dynamic of particulate matter. The measured singularity spectrum $f(\alpha)$ also shows its multifractal nature. Experimentally, the particulate matter is more intermittent than the passive scalar, which could be partially due to the \red{mesoscale movements} of the atmosphere, and also due to local sources, such as local industry activities.