We study statistical properties of atmospheric particulate matter fluctuations using six years of daily ${\mathrm{PM}}_{2.5}$ concentration data from 54 Indian cities. Despite diverse urban settings and heterogeneous climatic conditions, we find that the fluctuations show strikingly universal behavior 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 autocorrelation functions, and power spectral densities displaying a $\sim 1/f$ 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.

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• Climate research
• Scaling laws of complex systems
• Stochastic processes
• Langevin equation
• Time series analysis