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

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      Scientific Reports
      Nature Publishing Group UK

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          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.

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          Multifractal detrended fluctuation analysis of nonstationary time series

          , , (2002)
          We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series to those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima (WTMM) method, and show that the results are equivalent.
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            Impact of urbanization level on urban air quality: a case of fine particles (PM(2.5)) in Chinese cities.

            We examined and compared PM2.5 concentrations in urban and the surrounding regions, and further investigated the impact of urbanization on urban PM2.5 concentrations at the Chinese prefectures. Annual PM2.5 concentrations in most prefectures were greater than 10 μg/m(3), the air quality guideline of the World Health Organization. Those prefectures were mainly distributed along the east coast and southeast of Sichuan province; The urban PM2.5 concentrations ( [Formula: see text] ) in 85 cities were greater than (>10 μg/m(3)) those in the surrounding area. Those cities were mainly located in the Beijing-Sichuan and Shanghai-Guangxi belts. In addition, [Formula: see text] was less than (<0 μg/m(3)) that in surrounding areas in only 41 prefectures, which were located in western China or nearby mega cities; Significant positive correlations were found between [Formula: see text] and urban population (R(2) = 0.99, P < 0.05), and between [Formula: see text] and urban second industry fraction (R(2) = 0.71, P < 0.05), suggesting that urbanization had considerable impact on PM2.5 concentrations.
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              Focus on the emerging new fields of network physiology and network medicine

              Despite the vast progress and achievements in systems biology and integrative physiology in the last decades, there is still a significant gap in understanding the mechanisms through which (i) genomic, proteomic and metabolic factors and signaling pathways impact vertical processes across cells, tissues and organs leading to the expression of different disease phenotypes and influence the functional and clinical associations between diseases, and (ii) how diverse physiological systems and organs coordinate their functions over a broad range of space and time scales and horizontally integrate to generate distinct physiologic states at the organism level. Two emerging fields, network medicine and network physiology, aim to address these fundamental questions. Novel concepts and approaches derived from recent advances in network theory, coupled dynamical systems, statistical and computational physics show promise to provide new insights into the complexity of physiological structure and function in health and disease, bridging the genetic and sub-cellular level with inter-cellular interactions and communications among integrated organ systems and sub-systems. These advances form first building blocks in the methodological formalism and theoretical framework necessary to address fundamental problems and challenges in physiology and medicine. This ‘focus on’ issue contains 26 articles representing state-of-the-art contributions covering diverse systems from the sub-cellular to the organism level where physicists have key role in laying the foundations of these new fields.
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                Author and article information

                Contributors
                popwang619@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 August 2017
                31 August 2017
                2017
                : 7
                : 10109
                Affiliations
                [1 ]GRID grid.257160.7, College of Science/Agricultural Mathematical Modeling and Data Processing Center, Hunan Agricultural University, ; Changsha, P. R. China
                [2 ]ISNI 0000 0004 0402 6152, GRID grid.266820.8, Department of Mathematics and Statistics, University of New Brunswick, ; Fredericton, NB E3B 5A3 Canada
                [3 ]ISNI 0000 0001 1958 9263, GRID grid.268252.9, Department of Mathematics, Wilfrid Laurier University, ; Waterloo, ON N2L 3C5 Canada
                Article
                10419
                10.1038/s41598-017-10419-6
                5579243
                28860644
                7a8b8058-9140-4cdb-8e38-634da443d293
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 22 May 2017
                : 8 August 2017
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