Spatial heterogeneity of air pollution statistics

TL;DR

This study analyzes extensive European air pollution data, revealing that pollutant concentration PDFs are heavy-tailed and spatially heterogeneous, with parameters varying by location and pollutant type, indicating complex underlying statistical physics.

Contribution

It demonstrates that air pollution concentration PDFs are well approximated by q-exponential distributions with spatially varying parameters, highlighting heterogeneity in statistical properties across locations.

Findings

Pollutant concentration PDFs exhibit heavy tails.

Parameters q and λ vary significantly across locations.

Distinct patterns in parameter space depend on pollutant and environment.

Abstract

Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the time-varying statistics of different types of air pollutants are far from being fully understood. The observed probability density functions (PDFs) of concentrations depend very much on the spatial location and on the pollutant substance. In this paper, we analyse a large variety of data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($NO$), nitrogen dioxide ($NO2$) and particulate matter ($PM10$ and $PM2.5$) concentrations generically exhibit heavy tails and are asymptotically well approximated by $q$-exponential distributions with a given width parameter $\lambda$. We observe that the power-law parameter $q$ and the width parameter $\lambda$ vary widely for the different spatial locations. For each substance, we find different patterns of parameter clouds in the $(q, \lambda)$ plane. These depend on the type of pollutants and on the environmental characteristics (urban/suburban/rural/traffic/industrial/background). This means the effective statistical physics description of air pollution exhibits a strong degree of spatial heterogeneity.