scholarly journals Assessing the Wet Deposition Mechanism of Benzo(a)pyrene in the Atmosphere by MF-DCCA

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 331 ◽  
Author(s):  
Chunqiong Liu ◽  
Kai Shi ◽  
Jian Liang ◽  
Hongliang Huang
Keyword(s):  
Time Scale ◽  
Wet Deposition ◽  
Cross Correlation ◽  
Coefficient Of Determination ◽  
Scaling Properties ◽  
Temporal Scaling ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
One Year

Based on the 19 year observation from 1998 to 2016 at the Tsuan Wan and Central/Western District monitoring stations in Hong Kong, the aim of this paper was to assess the wet deposition pathway of Benzo(a)pyrene (BaP) on a large time-scale. In order to achieve this goal, multi-fractal detrended cross-correlation analysis (MF-DCCA) was used to characterize the long-term cross-correlations behaviors and multi-fractal temporal scaling properties between BaP (or PM2.5) and precipitation. The results showed that the relationships between BaP and precipitation (or PM2.5) displayed long-term cross-correlation at the time-scale ranging from one month to one year; no cross-correlation between each other was observed in longer temporal scaling regimes (greater than one year). These results correspond to the atmospheric circulation of the Asian monsoon system and are explained in detail. Similar dynamic processes of the wet deposition of BaP and PM2.5 suggested that the main removal process of atmospheric BaP was rainfall deposits of PM2.5-bound BaP. Furthermore, cross-correlations between BaP (or PM2.5) and precipitation at the long time-scale have a multi-fractal nature and long-term persistent power-law decaying behavior. The temporal evolutions of the multi-fractality were investigated by the approach of a sliding window. Based on the evolution curves of multi-fractal parameters, the wet deposition pathway of PM2.5-bound BaP is discussed. Finally, the contribution degree of wet deposition to PM2.5-bound BaP was derived from the coefficient of determination. It was demonstrated that about 45% and 60% of atmospheric BaP removal can be attributed to the wet deposition pathway of PM2.5-bound BaP for the Tsuan Wan and Central/Western District areas, respectively. The findings in this paper are of great significance for further study on the removal mechanism of atmospheric BaP in the future. The MF-DCCA method provides a novel approach to assessing the geochemical cycle dynamics of BaP.

scholarly journals Comparative analysis of contributions of wet deposition and photodegradation to the removal of atmospheric BaP by MFDCCA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunqiong Liu ◽  
Yuanyuan Guo ◽  
Kai Shi ◽  
Jiao Zhang ◽  
Bo Wu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Comparative Analysis ◽  
Wet Deposition ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Difference ◽  
One Year

AbstractBenzo [a] pyrene (BaP) in the atmosphere possess great carcinogenic potential to human health, and the understanding of its scavenging mechanisms has attracted considerable attention. In this work, a new quantitative method is proposed to make a comparative analysis of the long-term contributions of wet deposition and photodegradation to BaP removal based on multi-fractal detrended cross-correlation analysis (MFDCCA). According to the precipitation and global solar radiation (GSR) observations from 1998 to 2016 for two urban sites (Central/Western District and TsuenWan) in Hong Kong, the wet deposition and photodegradation of BaP are analyzed. Using MFDCCA method, long-term cross-correlation between precipitation/GSR and BaP are investigated. Moreover, the differences of multifractal features in cross-correlations of precipitation-BaP and GSR-BaP system are analyzed. Strong long-term persistence is observed in the cross-correlations for precipitation-BaP system in a one-year cycle; while cross-correlations between GSR and BaP show weak persistence over the whole timescale. Based on the meteorology in Hong Kong, this difference has been discussed. Then, contributions of wet deposition and photodegradation to atmospheric BaP removal are quantified based on MFDCCA method, which are further compared between summer and winter. The comparative analysis suggests that wet deposition plays a more significant role in the removal of atmospheric BaP. Specifically, in summer, the contributions of wet deposition are twice as much as that of photodegradation for both two sites; while in winter, the contribution of photodegradation is a little higher than that of wet deposition to BaP removal. Meanwhile, for wet deposition, the contributions in summer are about ten times greater than that in winter; while for photodegradation, the difference in contributions between summer and winter are relatively smaller. Furthermore, based on sliding window technique, the temporal evolutions in the contributions of wet deposition/photodegradation to BaP removal have been presented for both two sites. On this basis, it is discovered that the comprehensive contributions of wet deposition and photodegradation peak in June, and reach their lowest levels in December for both two sites. Quantifying the contribution of meteorological factors to the removal of atmospheric BaP is help for understanding its geochemical cycle.

Dynamic Cross-Correlations between Investors’ Attention and CSI300 Index Futures

2019 ◽  
Vol 18 (04) ◽  
pp. 1950022
Author(s):  
Xiong Xiong ◽  
Kewei Xu ◽  
Dehua Shen
Keyword(s):  
Cross Correlation ◽  
Futures Market ◽  
Relative Activity ◽  
Short Term ◽  
Index Futures ◽  
Search Volume ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

Using search volume on Baidu Index as the proxy for investors’ attention, we investigate the dynamic nonlinear relationship between investors’ attention and CSI300 index futures market. Multifractal detrend cross-correlation analysis (MF-DCCA) is employed to explore the multifractal features of the cross-correlations between investors’ attention and the return and relative activity of index futures market. We find that the power-law cross-correlations between investors’ attention and CSI300 index futures market are stronger in the short term than in the long term, and the cross-correlations are significantly multifractal. Precisely, the cross-correlation between abnormal search volume (ASV) and the relative activity is persistent, and the cross-correlation between ASV and return of IF is persistent in the short term but weakly anti-persistent in the long term. Besides, we also find that, with the restriction on index futures market, the cross-correlations between investors’ attention and CSI300 index futures market become less stable.

scholarly journals Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Gang-Jin Wang ◽  
Chi Xie ◽  
Shou Chen ◽  
Feng Han
Keyword(s):  
Multifractal Analysis ◽  
Cross Correlation ◽  
Market Dynamics ◽  
Gas Oil ◽  
Short Term ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Rolling Windows ◽  
Oil Gas

We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas), oil and CO2(Oil-CO2), and gas andCO2(Gas-CO2) based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, andCO2during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA) method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.

MULTIFRACTAL CROSS-CORRELATION ANALYSIS BASED ON STATISTICAL MOMENTS

Fractals ◽  
2012 ◽  
Vol 20 (03n04) ◽  
pp. 271-279 ◽  
Author(s):  
JING WANG ◽  
PENGJIAN SHANG ◽  
WEIJIE GE
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Moving Average ◽  
Statistical Moments ◽  
Comparative Performance ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Better Than

We introduce a new method, multifractal cross-correlation analysis based on statistical moments (MFSMXA), to investigate the long-term cross-correlations and cross-multifractality between time series generated from complex system. Efficiency of this method is shown on multifractal series, comparing with the well-known multifractal detrended cross-correlation analysis (MFXDFA) and multifractal detrending moving average cross-correlation analysis (MFXDMA). We further apply this method on volatility time series of DJIA and NASDAQ indices, and find some interesting results. The MFSMXA has comparative performance with MFXDMA and sometimes perform slightly better than MFXDFA. Multifractal nature exists in volatility series. In addition, we find that the cross-multifractality of volatility series is mainly due to their cross-correlations, via comparing the MFSMXA results for original series with those for shuffled series.

scholarly journals Connectedness between crude oil and US equities: The impact of COVID-19 pandemic

2021 ◽  
Author(s):  
Aktham Maghyereh ◽  
hussein abdoh
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Moving Average ◽  
Equity Market ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Impact ◽  
Dynamical Connection

Abstract In this paper, we exploit multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the impact of COVID-19 pandemic on the cross-correlations between oil and US equity market (as represented by the S&P 500 index). First, we examine the detrended moving average cross-correlation coefficient between oil and S&P 500 returns before and during the COVID-19 pandemic. The correlation analysis shows that US stock markets became more correlated with oil during the pandemic in the long term. Second, we find that the pandemic has caused an increase in the long range cross correlations over the small fluctuations. Third, the MF-DCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. In sum, the pandemic caused a closer correlation between oil and US equity in the long range and a deeper dynamical connection between oil and US equity markets as indicated by the multifractality tests.

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS FOR LARGE-SCALE WAREHOUSE-OUT BEHAVIORS

Fractals ◽  
2015 ◽  
Vol 23 (04) ◽  
pp. 1550044 ◽  
Author(s):  
CAN-ZHONG YAO ◽  
JI-NAN LIN ◽  
XU-ZHOU ZHENG
Keyword(s):  
Correlation Analysis ◽  
Large Scale ◽  
Cross Correlation ◽  
Correlation Property ◽  
Long Term Memory ◽  
Correlation Relationship ◽  
Term Memory ◽  
Wire Rod ◽  
Cross Correlation Analysis

Based on cross-correlation algorithm, we analyze the correlation property of warehouse-out quantity of different warehouses, respectively, and different products of each warehouse. Our study identifies that significant cross-correlation relationship for warehouse-out quantity exists among different warehouses and different products of a warehouse. Further, we take multifractal detrended cross-correlation analysis for warehouse-out quantity among different warehouses and different products of a warehouse. The results show that for the warehouse-out behaviors of total amount, different warehouses and different products of a warehouse significantly follow multifractal property. Specifically for each warehouse, the coupling relationships of rebar and wire rod reveal long-term memory characteristics, no matter for large fluctuation or small one. The cross-correlation effect on long-range memory property among warehouses probably has less to do with product types,and the long-term memory of YZ warehouse is greater than others especially in total amount and wire rod product. Finally, we shuffle and surrogate data to explore the source of multifractal cross-correlation property in logistics system. Taking the total amount of warehouse-out quantity as example, we confirm that the fat-tail distribution of warehouse-out quantity sequences is the main factor for multifractal cross-correlation. Through comparing the performance of the multifractal detrended cross-correlation analysis (MF-DCCA), centered multifractal detrending moving average cross-correlation analysis (MF-X-DMA) algorithms, the forward and backward MF-X-DMA algorithms, we find that the forward and backward MF-X-DMA algorithms exhibit a better performance than the other ones.

Kinematic Analysis of Manual Tracking in Monkeys: Characterization of Movement Intermittencies During a Circular Tracking Task

2004 ◽  
Vol 91 (2) ◽  
pp. 901-911 ◽  
Author(s):  
A. V. Roitman ◽  
S. G. Massaquoi ◽  
K. Takahashi ◽  
T. J. Ebner
Keyword(s):  
Cross Correlation ◽  
Manual Tracking ◽  
Speed Profile ◽  
Visually Guided ◽  
Scaling Properties ◽  
Direction Error ◽  
Speed Error ◽  
Cross Correlation Analysis ◽  
Profile Control ◽  
Acceleration Force

Segmentation of the velocity profiles into the submovements has been observed in reaching and tracking limb movements and even in isometric tasks. Submovements have been implicated in both feed-forward and feedback control. In this study, submovements were analyzed during manual tracking in the nonhuman primate with the focus on the amplitude-duration scaling of submovements and the error signals involved in their control. The task consisted of the interception and visually guided pursuit of a target moving in a circle. The submovements were quantified based on their duration and amplitude in the speed profile. Control experiments using passive movements demonstrated that these intermittencies were not instrumentation artifacts. Submovements were prominent in both the interception and tracking phases and their amplitude scaled linearly with duration. The scaling factors increased with tracking speed at the same rate for both interception and pursuit. A cross-correlation analysis between a variety of error signals and the speed profile revealed that direction and speed errors were temporally coupled to the submovements. The cross-correlation profiles suggest that submovements are initiated when speed error reaches a certain limit and when direction error is minimized. The scaling results show that in monkeys submovements characterize both the interception and pursuit portions of the task and that these submovements have similar scaling properties consistent with 1) the concept of stereotypy and 2) adding constant acceleration/force at a specific tracking speed. The correlation results show involvement of speed and direction error signals in controlling the submovements.

scholarly journals Dollar’s Influence on Crude Oil and Gold Based on MF-DPCCA Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhonghui Ding ◽  
Kai Shi ◽  
Bin Wang
Keyword(s):  
Correlation Analysis ◽  
Crude Oil ◽  
Cross Correlation ◽  
Combined Effect ◽  
Analysis Method ◽  
Correlation Relationship ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method ◽  
Phase Randomization

This paper analyzed the influence of dollar on crude oil and gold based on the multifractal detrended partial cross-correlation analysis method. It showed that affected by the dollar, the crude oil and gold markets have a partial cross-correlation relationship which is stronger than their own cross-correlation. The partial cross-correlation is long-term and has multifractal characteristics. Through shuffled and Fourier-phase randomization, it is found that this multifractal feature is caused by the combined effect of the long-term cross-correlation between the returns and the fluctuation fat-tailed distribution, where the influence of the fat-tailed distribution is slightly greater than that of the long-term cross-correlation between the returns.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

Time-Series and Dynamic Cross-Correlations Analysis on Unexpected Information: Evidence from Media News and Online Postings

2019 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Yan Li ◽  
Xiangyu Kong ◽  
Xiao Li ◽  
Zuochao Zhang
Keyword(s):  
Mass Media ◽  
New Media ◽  
Trading Volume ◽  
Cross Correlation ◽  
Short Term ◽  
Cross Correlations ◽  
The Cross ◽  
The One ◽  
The Relationship

In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet.

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