Recently, air pollution such as the respirable particulate PM10 results in negative impact on human health. We study the non-linear cross-correlations between respiratory diseases and haze in South Korea, using multifractal detrended cross-correlation analysis (MF-DCCA). The empirical tests indicate that there exists cross-correlations between the monthly average PM10/Bronchitis time series pair, and monthly average PM10/Rhinitis time series pair. Metrics such as Hurst exponents, scaling exponents, and multifractal spectrums show that the multifractal characteristics of both the time series pairs are significant. In addition, we compare the degrees of multifractal spectrums and find that the cross-correlation of the time series pair PM10/Bronchitis is stronger than that of PM10/Rhinitis, which indicates that the monthly outpatient quantity of bronchitis is more sensitive to PM10 concentration. Furthermore, to identify the main source of multifractality for two time series pairs, we phase-randomize and shuffle the original series. The computational results demonstrate that fat-tailed distribution contributes to the multifractality between respiratory diseases and haze.
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Physics and Astronomy(all)
- Applied Mathematics