Superstatistical approach to air pollution statistics

TL;DR

This paper applies superstatistical models from nonequilibrium statistical mechanics to analyze the heavy-tailed distributions of NO and NO2 air pollution data in London, improving risk assessment and mitigation strategies.

Contribution

It introduces superstatistical models for air pollution data, specifically using $$ and inverse $$ superstatistics for NO and NO2, respectively, a novel approach in this context.

Findings

Concentrations exhibit heavy-tailed distributions.

NO follows $$ superstatistics, NO2 follows inverse $$ superstatistics.

Models enable precise high-pollution risk estimates.

Abstract

Air pollution by Nitrogen Oxides (NOx) is a major concern in large cities as it has severe adverse health effects. However, the statistical properties of air pollutants are not fully understood. Here, we use methods borrowed from nonequilibrium statistical mechanics to construct suitable superstatistical models for air pollution statistics. In particular, we analyze time series of Nitritic Oxide ($NO$) and Nitrogen Dioxide ($NO_2$) concentrations recorded at several locations throughout Greater London. We find that the probability distributions of concentrations have heavy tails and that the dynamics is well-described by $\chi^2$ superstatistics for $NO$ and inverse $\chi^2$ superstatistics for $NO_2$. Our results can be used to give precise risk estimates of high-pollution situations and pave the way to mitigation strategies.