JACIII Vol.23 No.5 pp. 823-830
doi: 10.20965/jaciii.2019.p0823


Asymmetric MF-DCCA Method Based on Fluctuation Conduction and its Application in Air Pollution in Hangzhou

Chaohui Xiang, Xiaozhen Hao, Wenhui Wang, and Zhenlong Chen

School of Statistics and Mathematics, Zhejiang Gongshang University
No. 18, Xuezheng Street, Xiasha University Town, Hangzhou 310018, China

Corresponding author

October 24, 2018
January 11, 2019
September 20, 2019
symmetric MF-DCCA, fluctuation conduction, causality, PM2.5

The study of the relationship between the concentration of PM2.5 and the local air quality index (AQI) is significant for the improvement of urban air quality. This study not only considered multifractal cross-correlation but also the fluctuation conduction mechanism. An asymmetric multifractal detrended cross-correlation analysis (MF-DCCA) method based on fluctuation conduction is introduced here to empirically explore the causality and conduction time between air quality factors and PM2.5 concentration. The empirical results indicate the existence of a bidirectional fluctuation conduction effect between PM2.5 and PM10, SO2, and NO2 in Hangzhou, China, with a conduction time of 30 hours; this effect is non-existent between PM2.5 and O3. In addition, there is a unidirectional fractal fluctuation conduction between PM2.5 and CO with a conduction time of 21 hours.

Cite this article as:
C. Xiang, X. Hao, W. Wang, and Z. Chen, “Asymmetric MF-DCCA Method Based on Fluctuation Conduction and its Application in Air Pollution in Hangzhou,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.5, pp. 823-830, 2019.
Data files:
  1. [1] R. T. Vassoler and G. F. Zebende, “DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity,” Physica A: Statistical Mechanics and its Applications, Vol.391, Issue 7, pp. 2438-2443, 2012.
  2. [2] D. D. Kang, D. I. Lee, B.-H. Kwon, K. Kim, and J.-K. Park, “Features of the detrended cross-correlation analysis in the time series between absorbable particulate matter and meteorological factors,” J. of the Korean Physical Society, Vol.63, Issue 1, pp. 10-17, 2013.
  3. [3] S. Hajian and M. S. Movahed, “Multifractal Detrended Cross-Correlation Analysis of sunspot numbers and river flow fluctuations,” Physica A: Statistical Mechanics and its Applications, Vol.389, Issue 21, pp. 4942-4957, 2010.
  4. [4] K. Shi, “Detrended cross-correlation analysis of temperature, rainfall, PM10 and ambient dioxins in Hong Kong,” Atmospheric Environment, Vol.97, pp. 130-135, 2014.
  5. [5] C. Shen, C. Li, and Y. Si, “A detrended cross-correlation analysis of meteorological and API data in Nanjing. China,” Physica A: Statistical Mechanics and its Applications, Vol.419, pp. 417-428, 2015.
  6. [6] G. Cao, Y. Han, Q. Li, and W. Xu, “Asymmetric MF-DCCA method based on risk conduction and its application in the Chinese and foreign stock markets,” Physica A: Statistical Mechanics and its Applications, Vol.468, pp. 119-130, 2017.
  7. [7] E. Baek and W. Brock, “A general test for non-linear Granger causality: bivariate model,” Working Paper, Iowa State University and University of Wisconsin–Madison, 1992.
  8. [8] C. Hiemstra and J. D. Jones, “Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation,” The J. of Finance, Vol.49, Issue 5, pp. 1639-1664, 1994.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 19, 2024