Multifractal Analysis of Atmospheric Pollution Dynamics
Summary
Atmospheric pollution exhibits intricate temporal and spatial variability driven by a combination of emission processes, meteorological conditions and chemical transformations. Multifractal analysis offers a powerful framework for quantifying the complex scaling behaviour and intermittent structures present in pollutant concentration time series. By evaluating generalised dimensions and singularity spectra, researchers can characterise the degree of heterogeneity across distinct time scales, identify long-range correlations and assess the contribution of extreme fluctuations. This approach has been applied to particulate matter (PM2.5 and PM10), gaseous pollutants and composite indices, revealing scale-free power laws, heavy-tailed distributions and indicators of persistence or anti-persistence in urban and regional contexts. Integration with models of transport dynamics and chemical processes enhances the interpretation of multifractal signatures, guiding the development of more accurate forecasting tools, informing mitigation strategies and deepening our understanding of pollution episodes from local to global scales.
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Multifractal Analysis of Atmospheric Pollution Dynamics publication trend
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Technical terms
Multifractal analysis: A method for describing data that exhibit a spectrum of scaling exponents, capturing variation in roughness or concentration fluctuations across multiple time scales.
Detrended fluctuation analysis: A technique to quantify long-range correlations in non-stationary time series by removing polynomial trends and assessing how root-mean-square fluctuations scale with segment size.
Hurst exponent: A parameter (H) that measures the tendency of a time series to exhibit persistence (H > 0.5), randomness (H ≈ 0.5) or anti-persistence (H < 0.5) in its fluctuations.
Fractal dimension: A numerical index that characterises the complexity of a pattern or time series by describing how detail in the data changes with observation scale.
Self-organised criticality: A property of certain complex systems that naturally evolve into a critical state, leading to scale-invariant behaviour and power-law distributions in event sizes or fluctuations.
References
- Multifractal Processes and Self-Organized Criticality of PM2.5 during a Typical Haze Period in Chengdu, China. Aerosol and Air Quality Research (2015).
- Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece. Environments (2019).
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