Description of the seasonal pattern in ozone concentration time series by using the strange attractor multifractal formalism
Ozone is a secondary pollutant that, at high concentrations in urban environment, affects human health. Thus, suitable knowledge of ozone dynamics is essential for avoiding undesirable effects on the population. Ten-minute ozone concentration time series recorded at Córdoba (southern Spain) in 2007, for January, April, July and October, have been analysed by using the strange attractor multifractal formalism with the aim of describing the seasonal pattern of this pollutant. The multifractal nature of ozone concentration was detected in a study of the mass exponent functions for autumn-winter and spring-summer seasons. Multifractal spectra showed a greater heterogeneity in the low values of the time series. The presence of rare values in ozone data sets was also investigated, showing that the multifractal approach provided considerable detailed information on the time series structure that completes the statistical descriptive analysis. © 2008 Springer Science+Business Media B.V.
Main Authors: | , , , |
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Format: | artículo biblioteca |
Language: | English |
Published: |
Springer
2010-01
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Online Access: | http://hdl.handle.net/10261/87299 |
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Summary: | Ozone is a secondary pollutant that, at high concentrations in urban environment, affects human health. Thus, suitable knowledge of ozone dynamics is essential for avoiding undesirable effects on the population. Ten-minute ozone concentration time series recorded at Córdoba (southern Spain) in 2007, for January, April, July and October, have been analysed by using the strange attractor multifractal formalism with the aim of describing the seasonal pattern of this pollutant. The multifractal nature of ozone concentration was detected in a study of the mass exponent functions for autumn-winter and spring-summer seasons. Multifractal spectra showed a greater heterogeneity in the low values of the time series. The presence of rare values in ozone data sets was also investigated, showing that the multifractal approach provided considerable detailed information on the time series structure that completes the statistical descriptive analysis. © 2008 Springer Science+Business Media B.V. |
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