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. 2022 Jul 6;19(14):8224.
doi: 10.3390/ijerph19148224.

Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

Linan Sun  1   2 , Antao Wang  3 , Jiayao Wang  2
Affiliations

Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

Linan Sun et al. Int J Environ Res Public Health. .

Abstract

This paper investigates the multifractal characteristics of six air pollutants using the coupling detrended fluctuation analysis method. The results show that coupling correlations exist among the air pollutants and have multifractal characteristics. The sources of multifractality are identified using the chi square test. The coupling strengths between different pollutants are quantified. In addition, the coupling contribution of a series in the haze system is calculated, and SO2, as the main pollutant, plays a key role in the pollution system. Moreover, the Kriging interpolation method is used to analyze the spatial characteristic on coupling contribution of SO2. The spatial analysis of coupling strength for air pollutants will provide an effective approach for pollution control.

Keywords: CDFA; coupling contribution; coupling correlation; multifractality; spatial interpolation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The distribution of automatic monitoring stations in Zhengzhou City and the location of Zhengzhou City in Henan Province.
Figure 2
Figure 2
Hourly average NO2, CO, O3, PM10, PM2.5 and SO2 data of Zhengzhou over the period from 24:00 17 December 2016 to 23:00 26 December 2016 (240 h).
Figure 2
Figure 2
Hourly average NO2, CO, O3, PM10, PM2.5 and SO2 data of Zhengzhou over the period from 24:00 17 December 2016 to 23:00 26 December 2016 (240 h).
Figure 3
Figure 3
The surface of fluctuation functions ln(Fxm1,…,xmn(q,s)) versus time scale ln(s) and moments q for (a) original pollutants time series, (b) shuffled pollutants time series and (c) surrogate pollutants time series in Zhengzhou, where q ranges from −20 to 20.
Figure 4
Figure 4
The behaviors of q~h(q) of original series, shuffled series and surrogate series of 6 pollutants at 9 monitoring stations in Zhengzhou using CDFA method.
Figure 4
Figure 4
The behaviors of q~h(q) of original series, shuffled series and surrogate series of 6 pollutants at 9 monitoring stations in Zhengzhou using CDFA method.
Figure 5
Figure 5
The behaviors of q~h(q) of 6 pollutants in nine monitoring stations in Zhengzhou after CDFA analysis, only the mentioned time series is shuffled and the other time series are kept unchanged.
Figure 5
Figure 5
The behaviors of q~h(q) of 6 pollutants in nine monitoring stations in Zhengzhou after CDFA analysis, only the mentioned time series is shuffled and the other time series are kept unchanged.
Figure 5
Figure 5
The behaviors of q~h(q) of 6 pollutants in nine monitoring stations in Zhengzhou after CDFA analysis, only the mentioned time series is shuffled and the other time series are kept unchanged.
Figure 6
Figure 6
And χ¯shuff2 values under CDFA analysis of shuffled series in each station for 0≤q≤20. (a) χshuff2 value in 9 stations, (b) χ¯shuff2 value in 9 stations.
Figure 7
Figure 7
The spatial distribution of (a) SO2 concentration and (b) χ¯shuff2 value (0≤q≤20).
See this image and copyright information in PMC

References

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