Universal Features in Atmospheric Particulate Matter Dynamics

TL;DR

This study reveals universal statistical features in atmospheric PM2.5 fluctuations across diverse Indian cities, demonstrating common distributional and dynamical properties after trend removal.

Contribution

It uncovers universal behaviour in particulate matter fluctuations and proposes a minimal stochastic model explaining these features.

Findings

Rescaled PDFs collapse onto a single exponential-Gaussian curve.

Residual time-series exhibit similar autocorrelation decay and 1/f spectral scaling.

A stochastic model accounts for the universal distribution and dynamics.

Abstract

We study statistical properties of atmospheric particulate matter fluctuations using six years of daily PM2.5 concentration data from fifty-four Indian cities. Despite diverse urban settings and heterogeneous climatic conditions, we find that the fluctuations show strikingly universal behaviour in both the distributional properties and temporal dynamics. After removing slow trends and seasonal components, the rescaled probability density functions of the residual fluctuations collapse onto a single curve and are well described by an exponentially modified Gaussian distribution. The rescaled residual time-series for all the cities further exhibit certain robust dynamical features, with similar decay of auto-correlation functions, and power spectral densities displaying a similar 1/f decay at the tails. Finally, we propose a minimal stochastic model for the residual dynamics, which explains the observed universal features -- the stationary distribution, temporal correlation, and spectral scaling.