The Temporal Variations of PM10 Concentration in Taipei: A Fractal Approach

Received: June 30, 2004
Revised: June 30, 2004
Accepted: June 30, 2004
Download Citation: RIS|BibTeX|https://doi.org/10.4209/aaqr.2004.07.0004  

Cite this article:
Ho, D.S., Juang, L.C., Liao, Y.Y., Wang, C.C., Lee, C.K., Hsu, T.C., Yang, S.Y. and Yu, C.C. (2004). The Temporal Variations of PM10 Concentration in Taipei: A Fractal Approach. Aerosol Air Qual. Res. 4: 38-55. https://doi.org/10.4209/aaqr.2004.07.0004

ABSTRACT

A one-year series of hourly average PM₁₀ observations, which was obtained from the urban and national park air monitoring station at Taipei (Taiwan), was analyzed by descriptive statistics and fractal methods to examine the temporal structures of PM₁₀ concentrations. It was found that all PM₁₀ measurements exhibited the characteristic right-skewed unimodal frequency distribution and long-term memory. A monodimensional fractal analysis was performed by transferring the PM₁₀ concentration time series into a useful compact form: the box-dimension (D_B)-threshold (T_h) and critical scale (C_S)-threshold (T_h) plots. Scale invariance was found in these time series and the box dimension was shown to be a decreasing function of the threshold PM₁₀ level, implying multifractal characteristics, (i.e., the weak and intense regions scale differently). To test this hypothesis, the PM₁₀ concentration time series were transferred into multifractal spectra, i.e., the τ(q)-q plots. The analysis confirmed the existence of multifractal characteristics. A simple two-scale Cantor set with unequal scales and weights was then used to fit the calculated τ(q)-q plots. This model fits well with the entire spectrum of scaling exponents for the examined PM₁₀ time series. The relationship between the fractal parameters and classical statistical characteristics, as well as some problems concerning the applicability of fractal methods on air pollution, are discussed.

Keywords: PM10; Box counting; Multifractal scaling analysis; Multifractal cascade model; Long-range dependence; Scale invariance