A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Nonlinear Analysis and Prediction of Coarse Particulate Matter Concentration in Ambient Air
This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time–series. The dynamics of the time–series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time–series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time–series using the two models also supported the same findings.
Nonlinear Analysis and Prediction of Coarse Particulate Matter Concentration in Ambient Air
This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time–series. The dynamics of the time–series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time–series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time–series using the two models also supported the same findings.
Nonlinear Analysis and Prediction of Coarse Particulate Matter Concentration in Ambient Air
Chelani, Asha B. (author) / Devotta, Sukumar (author)
Journal of the Air & Waste Management Association ; 56 ; 78-84
2006-01-01
7 pages
Article (Journal)
Electronic Resource
Unknown
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