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Multifractal characterization of Hong Kong air quality data

Anh, V. V. / Leung, Y. / Lam, K. C. / Yu, Z. G.

10.1002/env.648.abs

This article develops a theory for characterization of air quality data based on their measure representation. The measures are shown to be random cascades generated by an infinitely divisible distribution. This probability distribution is uniquely determined by the exponent K (q), q≥0, in the multifractal analysis of the cascade. The theory is applied to the SO₂, NO and NO₂ time series at seven locations of the Hong Kong Electric Co. monitoring network. The Gamma density function is demonstrated to give an excellent fit to the K (q) curve of each time series. This precise characterization therefore provides a needed tool for modelling pollution episodes as well as classification of the monitoring network. Copyright © 2004 John Wiley & Sons, Ltd.

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Multifractal characterization of Hong Kong air quality data

Anh, V. V. / Leung, Y. / Lam, K. C. / Yu, Z. G.

10.1002/env.648.abs

This article develops a theory for characterization of air quality data based on their measure representation. The measures are shown to be random cascades generated by an infinitely divisible distribution. This probability distribution is uniquely determined by the exponent K (q), q≥0, in the multifractal analysis of the cascade. The theory is applied to the SO₂, NO and NO₂ time series at seven locations of the Hong Kong Electric Co. monitoring network. The Gamma density function is demonstrated to give an excellent fit to the K (q) curve of each time series. This precise characterization therefore provides a needed tool for modelling pollution episodes as well as classification of the monitoring network. Copyright © 2004 John Wiley & Sons, Ltd.

Multifractal characterization of Hong Kong air quality data

Anh, V. V. / Leung, Y. / Lam, K. C. / Yu, Z. G.

10.1002/env.648.abs

This article develops a theory for characterization of air quality data based on their measure representation. The measures are shown to be random cascades generated by an infinitely divisible distribution. This probability distribution is uniquely determined by the exponent K (q), q≥0, in the multifractal analysis of the cascade. The theory is applied to the SO₂, NO and NO₂ time series at seven locations of the Hong Kong Electric Co. monitoring network. The Gamma density function is demonstrated to give an excellent fit to the K (q) curve of each time series. This precise characterization therefore provides a needed tool for modelling pollution episodes as well as classification of the monitoring network. Copyright © 2004 John Wiley & Sons, Ltd.

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Title:

Multifractal characterization of Hong Kong air quality data

Contributors:

Anh, V. V. (author) / Leung, Y. (author) / Lam, K. C. (author) / Yu, Z. G. (author)

Published in:

Environmetrics ; 16 ; 1-12

Publication date:

2005-02-01

Size:

12 pages

ISSN:

1099-095X , 1180-4009

DOI:

https://doi.org/10.1002/env.648

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

air quality data , multifractal analysis , air pollution , multifractal models

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