Multifractal detrended cross-correlation analysis on air pollutants of University of Hyderabad Campus, India

P. Manimaran and A.C. Narayana

Physica A: Statistical Mechanics and its Applications, 2018, vol. 502, issue C, 228-235

Abstract: In this paper, we study the multifractal characteristics and cross-correlation behaviour of Air Pollution Index (API) time series data through multifractal detrended cross-correlation analysis method. We analyse the daily API records of nine air pollutants of the university of Hyderabad campus for a period of three years (2013–2016). The cross-correlation behaviour has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, it is found that the cross-correlation analysis shows anti-correlation behaviour for all possible 36 bivariate time series. We also observe the existence of multifractal nature in all the bivariate time series in which many of them show strong multifractal behaviour. In particular, the hazardous particulate matter PM2.5 and inhalable particulate matter PM10 shows anti-correlated behaviour with all air pollutants.

Keywords: Air pollution index; Time series; Multi-fractal; Hurst exponent; Cross-correlation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:502:y:2018:i:c:p:228-235

DOI: 10.1016/j.physa.2018.02.160

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