Superstatistical approach to air pollution statistics

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 ($N{O}_2$) 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 $N{O}_2$. Our results can be used to give precise risk estimates of high-pollution situations and pave the way to mitigation strategies.

arXiv comment10 pages, including the appendices

References in corpus (10)

• Superstatistics
• Dynamical foundations of nonextensive statistical mechanics
• Brownian yet non-Gaussian diffusion: from superstatistics to subordination of diffusing diffusivities
• Non-Gaussian power grid frequency fluctuations characterized by Lévy-stable laws and superstatistics
• From time series to superstatistics
• Generalized entropies and the transformation group of superstatistics
• Defect turbulence and generalized statistical mechanics
• Statistics of 3-dimensional Lagrangian turbulence
• Maximum Entropy Principle in statistical inference: case for non-Shannonian entropies
• Superpositions of Probability Distributions

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• Data-Driven Load Profiles and the Dynamics of Residential Electric PowerConsumption
• Assessing fluctuations of long-memory environmental variables based on the robustified dynamic Orlicz risk
• Conditional maximum entropy and Superstatistics
• Spatial analysis of tails of air pollution PDFs in Europe
• Fluctuations of water quality time series in rivers follow superstatistics