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Spectral, multifractal and informational analysis of PM10 time series measured in Mexico City Metropolitan Area

Author

Listed:
  • Cárdenas-Moreno, P.R.
  • Moreno-Torres, L.R.
  • Lovallo, M.
  • Telesca, L.
  • Ramírez-Rojas, A.

Abstract

Atmospheric pollution in Mexico City Metropolitan Area (MCMA) represents a serious social and economic concern due to the very high population density. The most important industrial activities are the main responsible of the production of particulate matter PM10 that can damage seriously the human respiratory system. In the present study we investigate the dynamical properties of the time series of PM10 emissions recorded from 2005 to 2016 by the Automatic Atmospheric Monitoring Network (RAMA) in five zones of MCMA (NW, NE, CE, SW and SE). Several methods (periodogram, multifractal detrended fluctuation analysis and the Fisher–Shannon method) were applied to get the most exhaustive description of the dynamical characteristics of the series. Our findings point out to: (1) the existence of a twofold power law behavior in the power spectrum of all the series, indicating the co-existence of two different mechanisms underlying the time dynamics of PM10; (2) the emergence of the 7-day periodicity of anthropogenic nature; (3) the multifractal behavior of all the series, dominated by small fluctuations; (4) the identification of NE zone, which is also the most polluted one as characterized by the larger disorder.

Suggested Citation

  • Cárdenas-Moreno, P.R. & Moreno-Torres, L.R. & Lovallo, M. & Telesca, L. & Ramírez-Rojas, A., 2021. "Spectral, multifractal and informational analysis of PM10 time series measured in Mexico City Metropolitan Area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308438
    DOI: 10.1016/j.physa.2020.125545
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    References listed on IDEAS

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    1. Amato, Federico & Laib, Mohamed & Guignard, Fabian & Kanevski, Mikhail, 2020. "Analysis of air pollution time series using complexity-invariant distance and information measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. Meraz, M. & Alvarez-Ramirez, J. & Echeverria, J.C., 2017. "Asymmetric correlations in the ozone concentration dynamics of the Mexico City Metropolitan Area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 377-386.
    3. Ashutosh Chamoli & R. Yadav, 2015. "Multifractality in seismic sequences of NW Himalaya," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(1), pages 19-32, May.
    4. Telesca, Luciano & Lovallo, Michele, 2017. "On the performance of Fisher Information Measure and Shannon entropy estimators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 569-576.
    5. Meraz, M. & Rodriguez, E. & Femat, R. & Echeverria, J.C. & Alvarez-Ramirez, J., 2015. "Statistical persistence of air pollutants (O3,SO2,NO2 and PM10) in Mexico City," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 202-217.
    6. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
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