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Published on: December 15, 2018
Scaling characteristics in ozone concentration time series (OCTS)
Chung-Kung Lee1, Lain-Chuen Juang, Cheng-Cai Wang
1Department of Environmental Engineering, Green Environment R&D Center, Vanung University, Chung-Li 320, Taiwan, ROC. anthony@msa.vnu.edu.tw
Ozone concentration time series from Taipei exhibit multifractal characteristics, indicating complex scaling behavior. This analysis reveals long-term memory and cyclic patterns in air quality data.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Data Analysis
Background:
- Ozone (O3) is a key air pollutant with complex temporal dynamics.
- Understanding ozone concentration patterns is crucial for air quality management.
- Previous studies suggest chaotic behavior in ozone time series.
Purpose of the Study:
- To analyze the scaling structures of hourly average ozone concentrations.
- To investigate the presence of multifractal characteristics in ozone data.
- To model ozone concentration time series using fractal methods.
Main Methods:
- Analysis of one-year hourly ozone observations from Taipei.
- Application of descriptive statistics and fractal analysis techniques.
- Utilizing mono-fractal and multifractal spectral analyses (tau(q)-q plots).
Main Results:
- Ozone measurements showed right-skewed distributions, cyclic patterns, and long-term memory.
- Scale invariance was observed, with box dimension decreasing as threshold ozone increased.
- Multifractal characteristics were confirmed, well-fitted by a two-scale Cantor set model.
Conclusions:
- Ozone concentration time series exhibit multifractal properties.
- Fractal analysis provides a robust method for characterizing complex air quality data.
- A chaotic multifractal approach may offer further insights into ozone dynamics.
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