三维空气污染物系统的多元多重分形分析
|
摘要
运用最新提出的多元多重分形去趋势波动分析法(MV-MFDFA)研究北京和上海两地的三维空气污染物(PM_(2.5)、PM_(10)、NO_2)浓度指数系统的非线性波动特征及动态复杂性。实证结果表明,两地污染物系统及其分量序列均存在长程相关性和时变的多重分形特征,相比较而言,北京污染物系统序列的多重分形强度更大,而上海污染物系统的长程相关性更强。此外,两地污染物系统在不同时间标度下呈现出不同的多重分形特征。最后,研究了两地污染物系统序列具有多重分形性的原因,指出胖尾概率分布和长程相关性是导致序列多重分形性的共同原因,且胖尾概率分布的贡献更大。
Using the newly proposed method of the multivariate multifractal detrended fluctuation analysis(MV-MFDFA),the nonlinear fluctuation characteristics and dynamic complexity of the series of the three-dimensional air pollutant(PM_(2.5),PM_(10), NO_2) concentration index systems for Beijing and Shanghai were studied. The empirical results show that both pollutant systems and their component series have the long-range correlation and time-varying multifractality. In comparison, the multifractal strength of the series of Beijing pollutant system is greater than that of Shanghai, while the long-range of Shanghai pollutant system is stronger than that of Beijing. In addition, it was found that the two pollutant systems present different multifractal features at different time scales. Finally, the reasons for the multifractality of the two pollutant systems were investigated, and it was found that both the fat-tailed probability distribution and long-range correlation are common causes for the multifractality, and the fat-tailed probability distribution has a major effect on the multifractality.
Using the newly proposed method of the multivariate multifractal detrended fluctuation analysis(MV-MFDFA),the nonlinear fluctuation characteristics and dynamic complexity of the series of the three-dimensional air pollutant(PM_(2.5),PM_(10), NO_2) concentration index systems for Beijing and Shanghai were studied. The empirical results show that both pollutant systems and their component series have the long-range correlation and time-varying multifractality. In comparison, the multifractal strength of the series of Beijing pollutant system is greater than that of Shanghai, while the long-range of Shanghai pollutant system is stronger than that of Beijing. In addition, it was found that the two pollutant systems present different multifractal features at different time scales. Finally, the reasons for the multifractality of the two pollutant systems were investigated, and it was found that both the fat-tailed probability distribution and long-range correlation are common causes for the multifractality, and the fat-tailed probability distribution has a major effect on the multifractality.
引文
[1]Lopez,Galinato G I,Islam A.Fiscal spending and the environment:theory and empirics[J].Journal of Environmental Economics&Management,2011,62(2):180-198.
[2]Shi K,Liu C Q,Ai N S,et al.Using three methods to investigate time-scaling properties in air pollution indexes time series[J].Nonlinear Analysis:Real World Applications,2008,9(2):693-707.
[3]Windsor H L,Toumi R.Scaling and persistence of UK pollution[J].Atmospheric Environment,2001,35(27):4545-4556.
[4]秦廷双,何红弟.城郊气象因素与NOx、PM10的多重分形分析[J].环境工程学报,2017,11(5):2960-2966.Qin Tingshuang,He Hongdi.Multi-fractal analysis of meteorological factors and NOxand PM10in suburban areas[J].Chinese Journal of Environmental Engineering,2017,11(5):2960-2966.
[5]Liu Z,Wang L,Zhu H.A time-scaling property of air pollution indices:a case study of Shanghai,China[J].Atmospheric Pollution Research,2015,6(5):886-892.
[6]Lee C K.Multifractal characteristics in air pollutant concentration time series[J].Water Air&Soil Pollution,2002,135(1/2/3/4):389-409.
[7]Anh V,Lam K C,Leung Y,et al.Multifractal analysis of Hong Kong air quality data[J].Environmetrics,2015,11(2):139-149.
[8]黄毅,刘春琼,史凯,等.灰霾消散前后PM10浓度大幅波动的多重分形分析[J].环境科学与技术,2016,39(1):140-146.Huang Yi,Liu Chunqiong,Shi Kai,et al.Multifractal analysis of large fluctuations in PM10concentration before and after emission of ashes[J].Environmental Science&Technology,2016,39(1):140-146.
[9]Zhang C,Ni Z,Ni L.Multifractal detrended cross-correlation analysis between PM2.5and meteorological factors[J].Physica A,2015,438:114-123.
[10]Shen C H,Huang Y,Yan Y N.An analysis of multifractal characteristics of API time series in Nanjing,China[J].Physica A,2016,451:171-179.
[11]Dong Q,Wang Y,Li P.Multifractal behavior of an air pollutant time series and the relevance to the predictability[J].Environmental Pollution,2016,222:444-457.
[12]He H D,Pan W,Lu W Z,et al.Multifractal property and long-range cross-correlation behavior of particulate matters at urban traffic intersection in Shanghai[J].Stochastic Environmental Research&Risk Assessment,2016,30(5):1515-1525.
[13]Xiong H,Shang P.Detrended fluctuation analysis of multivariate time series[J].Communications in Nonlinear Science&Numerical Simulation,2017,42:12-21.
[14]Zhang X,Zeng M,Meng Q.Multivariate multifractal detrended fluctuation analysis of 3D wind field signals[J].Physica A,2018,490:513-523.
[15]Kantelhardt J W,Zschiegner S A,Kosscielny-Bunde E,et al.Multifractal detrended fluctuation analysis of nonstationary time series[J].Physica A,2002,316(1):87-114.
[16]Peng C K,Buldyrev S V,Havlin S,et al.Mosaic organization of DNA nucleotides[J].Physical Review,1994,49(2):1685-1689.
[2]Shi K,Liu C Q,Ai N S,et al.Using three methods to investigate time-scaling properties in air pollution indexes time series[J].Nonlinear Analysis:Real World Applications,2008,9(2):693-707.
[3]Windsor H L,Toumi R.Scaling and persistence of UK pollution[J].Atmospheric Environment,2001,35(27):4545-4556.
[4]秦廷双,何红弟.城郊气象因素与NOx、PM10的多重分形分析[J].环境工程学报,2017,11(5):2960-2966.Qin Tingshuang,He Hongdi.Multi-fractal analysis of meteorological factors and NOxand PM10in suburban areas[J].Chinese Journal of Environmental Engineering,2017,11(5):2960-2966.
[5]Liu Z,Wang L,Zhu H.A time-scaling property of air pollution indices:a case study of Shanghai,China[J].Atmospheric Pollution Research,2015,6(5):886-892.
[6]Lee C K.Multifractal characteristics in air pollutant concentration time series[J].Water Air&Soil Pollution,2002,135(1/2/3/4):389-409.
[7]Anh V,Lam K C,Leung Y,et al.Multifractal analysis of Hong Kong air quality data[J].Environmetrics,2015,11(2):139-149.
[8]黄毅,刘春琼,史凯,等.灰霾消散前后PM10浓度大幅波动的多重分形分析[J].环境科学与技术,2016,39(1):140-146.Huang Yi,Liu Chunqiong,Shi Kai,et al.Multifractal analysis of large fluctuations in PM10concentration before and after emission of ashes[J].Environmental Science&Technology,2016,39(1):140-146.
[9]Zhang C,Ni Z,Ni L.Multifractal detrended cross-correlation analysis between PM2.5and meteorological factors[J].Physica A,2015,438:114-123.
[10]Shen C H,Huang Y,Yan Y N.An analysis of multifractal characteristics of API time series in Nanjing,China[J].Physica A,2016,451:171-179.
[11]Dong Q,Wang Y,Li P.Multifractal behavior of an air pollutant time series and the relevance to the predictability[J].Environmental Pollution,2016,222:444-457.
[12]He H D,Pan W,Lu W Z,et al.Multifractal property and long-range cross-correlation behavior of particulate matters at urban traffic intersection in Shanghai[J].Stochastic Environmental Research&Risk Assessment,2016,30(5):1515-1525.
[13]Xiong H,Shang P.Detrended fluctuation analysis of multivariate time series[J].Communications in Nonlinear Science&Numerical Simulation,2017,42:12-21.
[14]Zhang X,Zeng M,Meng Q.Multivariate multifractal detrended fluctuation analysis of 3D wind field signals[J].Physica A,2018,490:513-523.
[15]Kantelhardt J W,Zschiegner S A,Kosscielny-Bunde E,et al.Multifractal detrended fluctuation analysis of nonstationary time series[J].Physica A,2002,316(1):87-114.
[16]Peng C K,Buldyrev S V,Havlin S,et al.Mosaic organization of DNA nucleotides[J].Physical Review,1994,49(2):1685-1689.