Abstract

In order to investigate the time-dependent cross-correlations of fine particulate (PM2.5) series among neighboring cities in Northern China, in this paper, we propose a new cross-correlation coefficient, the time-lagged q-L dependent height crosscorrelation coefficient (denoted by pq(τ, L)), which incorporates the time-lag factor and the fluctuation amplitude information into the analogous height cross-correlation analysis coefficient. Numerical tests are performed to illustrate that the newly proposed coefficient ρq(τ, L) can be used to detect cross-correlations between two series with time lags and to identify different range of fluctuations at which two series possess cross-correlations. Applying the new coefficient to analyze the time-dependent cross-correlations of PM2.5 series between Beijing and the three neighboring cities of Tianjin, Zhangjiakou, and Baoding, we find that time lags between the PM2.5 series with larger fluctuations are longer than those between PM2.5 series withsmaller fluctuations. Our analysis also shows that cross-correlations between the PM2.5 series of two neighboring cities are significant and the time lags between two PM2.5 series of neighboring cities are significantly non-zero. These findings providenew scientific support on the view that air pollution in neighboring cities can affect one another not simultaneously but with a time lag.

Highlights

  • As a cost to rapid development of economics and progress of technology after World War II, the environmental pollution, air pollution produced by industry exhaust, smoke dust, and coal combustion, has become a serious world-wide problem[1]

  • The so-called time-lagged detrended cross-correlation analysis (DCCA) cross-correlation coefficient was proposed by Shen et al.[36], which can be used to detect time-dependent cross-correlations between the air pollution index (API) and wind speed but fails to recognize the range of fluctuation amplitudes that contributed to those cross-correlations

  • To fully detect and quantify possible cross-correlations among the PM2.5 series mentioned above and uncover the potential time lags embedded in those cross-correlations, in this work, we propose a new cross-correlation coefficient by incorporating the time-lag factor and fluctuation information into the latest analogous height cross-correlation analysis (AHXA) coefficient ρ(L) introduced by Wang et al.[21]

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Summary

Introduction

As a cost to rapid development of economics and progress of technology after World War II, the environmental pollution, air pollution produced by industry exhaust, smoke dust, and coal combustion, has become a serious world-wide problem[1]. These two experiments clearly illustrate that the proposed time-lagged q-L dependent AHXA coefficient ρq(τ,L) can capture fluctuation information and potential time-delay in cross-correlations.

Results
Conclusion
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