Dong Qingli, Wang Yong, Li Peizhi
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China; Postdoctoral Research Station of Dongbei University of Finance and Economics, Dalian 116025, China.
Environ Pollut. 2017 Mar;222:444-457. doi: 10.1016/j.envpol.2016.11.090. Epub 2016 Dec 21.
Compared with the traditional method of detrended fluctuation analysis, which is used to characterize fractal scaling properties and long-range correlations, this research provides new insight into the multifractality and predictability of a nonstationary air pollutant time series using the methods of spectral analysis and multifractal detrended fluctuation analysis. First, the existence of a significant power-law behavior and long-range correlations for such series are verified. Then, by employing shuffling and surrogating procedures and estimating the scaling exponents, the major source of multifractality in these pollutant series is found to be the fat-tailed probability density function. Long-range correlations also partly contribute to the multifractal features. The relationship between the predictability of the pollutant time series and their multifractal nature is then investigated with extended analyses from the quantitative perspective, and it is found that the contribution of the multifractal strength of long-range correlations to the overall multifractal strength can affect the predictability of a pollutant series in a specific region to some extent. The findings of this comprehensive study can help to better understand the mechanisms governing the dynamics of air pollutant series and aid in performing better meteorological assessment and management.
与用于表征分形标度特性和长程相关性的传统去趋势波动分析方法相比，本研究使用谱分析和多重分形去趋势波动分析方法，对非平稳空气污染物时间序列的多重分形性和可预测性提供了新的见解。首先，验证了此类序列存在显著的幂律行为和长程相关性。然后，通过采用重排和替代程序并估计标度指数，发现这些污染物序列中多重分形性的主要来源是肥尾概率密度函数。长程相关性也部分促成了多重分形特征。随后，从定量角度通过扩展分析研究了污染物时间序列的可预测性与其多重分形性质之间的关系，发现长程相关性的多重分形强度对整体多重分形强度的贡献在一定程度上会影响特定区域内污染物序列的可预测性。这项综合研究的结果有助于更好地理解控制空气污染物序列动态的机制，并有助于进行更好的气象评估和管理。
