Detecting PM2.5’s Correlations between Neighboring Cities Using a Time-Lagged Cross-Correlation Coefficient

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 p q(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 ρ q(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.

Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

“…Finally, we summarize and draw some general conclusions. In view of the abovementioned interdisciplinary research by other authors [22,24,25,[27][28][29][30], through the conclusions from our present work, we would like also to support advantages of multifractal detrended cross-correlation method and its wide applications to study any time series with nonlinear correlations, not only in the foreign exchange market but also across other fields of pure and applied sciences.…”

“…We would like to emphasize that our method based on detrended cross-correlation analysis is quite novel and only recently a plethora of applications started to emerge across many fields of nonlinear correlations studies, including meteorological data [22], electricity spot market [23], effects of weather on agricultural market [24], stock markets [25], cryptocurrency markets [26], electroencephalography (EEG) signals [27], electrocardiography (ECG) and arterial blood pressure [28] as well as air pollution [29,30]. Such wide interest across different fields of research in application of detrended cross-correlation analysis to nonlinear time series studies serves as an additional strong motivation for elucidating such analysis in terms of its potential and limitations.…”

Study on the influence of surrounding urban SO2, NO2, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

“…In particular, the distance is likely to cause the oxide propagation time lag. 38 Because these cities are close to Beijing and their values are the daily mean concentrations, this study ignores this problem. Their statistical descriptions of data are shown in Table 1 (the first column uses area acronyms instead).…”

A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities

“…A common sense is that smog produced at one source place can spread to surrounding areas [6,7,8,9,10]. Therefore, it is more practical to explore the dependence of air pollution indicators among adjacent cities as it helps assess the causes of local smog and its spread behavior.…”

“…It has been found by a newly proposed time-lagged cross-correlation coefficient in Ref. [10] that there are different degrees of correlation for PM2.5 series between four neighboring cities in Northern China. However, what has not been investigated is how the PM2.5 series of one city depends on those of the neighbouring cities.…”