Chelani Asha B, Devotta Sukumar
Air Pollution Control Division, National Environmental Engineering Research Institute, Nagpur, India.
J Air Waste Manag Assoc. 2006 Jan;56(1):78-84. doi: 10.1080/10473289.2006.10464432.
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.
本研究试图运用非线性动力学理论的概念来表征和预测环境空气中的粗颗粒物（PM10）浓度。利用1999年至2002年期间在印度孟买某地点每日观测到的PM10数据来研究混沌理论的适用性。首先，使用自相关函数和傅里叶功率谱来分析时间序列的行为。还通过关联积分分析和相空间重构对时间序列的动力学进行了研究。然后基于重构的相空间使用局部多项式逼近获得非线性预测。接着将结果与自回归模型进行比较。非线性分析结果表明PM10时间序列中存在混沌特征。还观察到非线性局部逼近优于自回归模型，因为自回归模型的观测预测相对误差大于局部逼近模型。使用这两种模型为预测时间序列计算的非线性动力学不变量也支持了相同的发现。
