Spatial analysis of tails of air pollution PDFs in Europe

TL;DR

This study analyzes the spatial and temporal variability of air pollution PDFs across Europe, revealing heavy-tailed distributions that vary regionally and are well modeled by q-exponential functions, with implications for understanding pollution dynamics.

Contribution

It provides a comprehensive spatial analysis of air pollution PDFs in Europe, demonstrating their heavy-tailed nature and regional variability using q-exponential modeling.

Findings

Pollution PDFs exhibit heavy tails across European sites.

Parameters q and λ vary significantly by region.

Spatial patterns correlate with geographical features.

Abstract

Outdoor air pollution is estimated to cause a huge number of premature deaths worldwide, it catalyses many diseases on a variety of time scales, and it has a detrimental effect on the environment. In light of these impacts it is necessary to obtain a better understanding of the dynamics and statistics of measured air pollution concentrations, including temporal fluctuations of observed concentrations and spatial heterogeneities. Here we present an extensive analysis for measured data from Europe. The observed probability density functions (PDFs) of air pollution concentrations depend very much on the spatial location and on the pollutant substance. We analyse a large number of time series data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($NO$), nitrogen dioxide ($NO_{2}$) and particulate matter ($PM_{10}$ and $PM_{2.5}$) concentrations generically exhibit heavy tails. These are asymptotically well approximated by $q$-exponential distributions with a given entropic index $q$ and width parameter $\lambda$. We observe that the power-law parameter $q$ and the width parameter $\lambda$ vary widely for the different spatial locations. We present the results of our data analysis in the form of a map that shows which parameters $q$ and $\lambda$ are most relevant in a given region. A variety of interesting spatial patterns is observed that correlate to properties of the geographical region. We also present results on typical time scales associated with the dynamical behaviour.