Abstract
Three chaotic indicators, namely the correlation dimension, the Lyapunov exponent, and the Kolmogorov entropy, are estimated for one-year long hourly average NO (nitrogen monoxide), CO (carbon monoxide), SO2 (sulfur dioxide), PM10 (particles with an aerodynamic diameter of approximately 10 μm or less), and NO2 (nitrogen dioxide) concentration to examine the possible chaotic characteristics in the air pollutant concentration (APC) time series. The presence of chaos in the examined APC time series is evident with the low correlation dimensions (3.42-4.71), the positive values of the largest Lyapunov exponent (0.128-0.427), and the positive Kolmogorov entropies (0.628-0.737). Since the existence of multifractal characteristics in the above time series has been confirmed in our previous investigations, the presence of chaotic behavior identified in the current study suggests the possibility of a chaotic multifractal approach for APC time series characterization. Some problems concerning the applicability of chaos analysis in air pollution are also discussed.
Highlights
Air quality changes related to human action can be investigated by long-term and large-area monitoring
We analyze the hourly average air pollutant concentration (APC) data collected at the Chung-Shan air quality monitoring station, Taipei (Taiwan), from January 1998 to December 1998, to investigate the presence of chaos and the possibility of a chaotic multifractal approach for APC time series characterization
Since the multifractal characteristics in the time series used in this study have been detected in our previous investigations (Lee, 2002; Lee et al, 2003a and 2003b), the results shown here provide a positive evidence for the coexistence of multifractal and chaotic behaviors in the APC time series
Summary
Air quality changes related to human action can be investigated by long-term and large-area monitoring. If positive evidence of the coexistence of chaos and fractal behavior can be provided, the APC time series characterization can be viewed from a new perspective: the chaotic multifractal approach, as reported by Sivakumar (2001) for rainfall characterization.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
