Improving graph-based detection of singular events for photochemical smog agents.

Recently, a set of graph-based tools have been introduced for the identification of singular events of O3, NO2 and temperature time series, as well as description of their dynamics. These are based on the use of the Visibility Graphs (VG). In this work, an improvement of the original approach is proposed, being called Upside-Down Visibility Graph (UDVG). It adds the possibility of investigating the singular lowest episodes, instead of the highest. Results confirm the applicability of the new method for describing the multifractal nature of the underlying O3, NO2, and temperature. Asymmetries in the NO2 degree distribution are observed, possibly due to the interaction with different chemicals. Furthermore, a comparison of VG and UDVG has been performed and the outcomes show that they describe opposite subsets of the time series (low and high values) as expected. The combination of the results from the two networks is proposed and evaluated, with the aim of obtaining all the information at once. It turns out to be a more complete tool for singularity detection in photochemical time series, which could be a valuable asset for future research.

[1]  F. J. Jiménez-Hornero,et al.  Checking complex networks indicators in search of singular episodes of the photochemical smog. , 2020, Chemosphere.

[2]  H. Dop,et al.  Photochemical production of ozone in Western Europe (1971–1975) and its relation to meteorology , 1977 .

[3]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[4]  Chuang Liu,et al.  How events determine spreading patterns: information transmission via internal and external influences on social networks , 2015, ArXiv.

[5]  Eduardo Gutiérrez de Ravé,et al.  Visibility graphs of ground-level ozone time series: A multifractal analysis. , 2019, The Science of the total environment.

[6]  Gurmukh Singh,et al.  Multifractal analysis of multiparticle emission data in the framework of visibility graph and sandbox algorithm , 2018 .

[7]  J. C. Nuño,et al.  The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion , 2009, 0901.0888.

[8]  J. Adame,et al.  Spatial and temporal variation of surface ozone, NO and NO2 at urban, suburban, rural and industrial sites in the southwest of the Iberian Peninsula , 2014, Environmental Monitoring and Assessment.

[9]  Luciano Telesca,et al.  Visibility graph analysis of wind speed records measured in central Argentina , 2012 .

[10]  Lucas Lacasa,et al.  Description of stochastic and chaotic series using visibility graphs. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Yu Song,et al.  An economic assessment of the health effects and crop yield losses caused by air pollution in mainland China. , 2017, Journal of environmental sciences.

[12]  F. Ling,et al.  The association between ambient particulate matters, nitrogen dioxide, and childhood scarlet fever in Hangzhou, Eastern China, 2014-2018. , 2020, Chemosphere.

[13]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[14]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[15]  Shlomo Havlin,et al.  Origins of fractality in the growth of complex networks , 2005, cond-mat/0507216.

[16]  Pengjian Shang,et al.  The application of Hölder exponent to traffic congestion warning , 2006 .

[17]  A. Turner,et al.  From Isovists to Visibility Graphs: A Methodology for the Analysis of Architectural Space , 2001 .

[18]  J. Kurths,et al.  Complex network approaches to nonlinear time series analysis , 2019, Physics Reports.

[19]  M. Kampa,et al.  Human health effects of air pollution. , 2008, Environmental pollution.

[20]  Miroslaw J. Skibniewski,et al.  Characterizing time series of near-miss accidents in metro construction via complex network theory , 2017 .

[21]  C J Stam,et al.  Characterization of anatomical and functional connectivity in the brain: a complex networks perspective. , 2010, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[22]  Jonathan F. Donges,et al.  Visibility graph analysis of geophysical time series: Potentials and possible pitfalls , 2012, Acta Geophysica.

[23]  Wei Yan,et al.  Maternal exposure to NO2 enhances airway sensitivity to allergens in BALB/c mice through the JAK-STAT6 pathway. , 2018, Chemosphere.

[24]  J. Carretero,et al.  Analyses of ozone in urban and rural sites in Málaga (Spain). , 2004, Chemosphere.

[25]  Paul J. Laurienti,et al.  A New Measure of Centrality for Brain Networks , 2010, PloS one.

[26]  Y. Qin,et al.  Weekend/weekday differences of ozone, NOx, Co, VOCs, PM10 and the light scatter during ozone season in southern California , 2004 .

[27]  A. B. Ariza-Villaverde,et al.  Can complex networks describe the urban and rural tropospheric O3 dynamics? , 2019, Chemosphere.

[28]  H. He,et al.  Multifractal analysis of interactive patterns between meteorological factors and pollutants in urban and rural areas , 2017 .

[29]  Chenquan Gan,et al.  Propagation of computer virus both across the Internet and external computers: A complex-network approach , 2014, Commun. Nonlinear Sci. Numer. Simul..

[30]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[31]  Joint multifractal analysis of the influence of temperature and nitrogen dioxide on tropospheric ozone , 2015, Stochastic Environmental Research and Risk Assessment.

[32]  Lucas Lacasa,et al.  Network structure of multivariate time series , 2014, Scientific Reports.

[33]  S. Loutridis.  An algorithm for the characterization of time-series based on local regularity , 2007 .

[34]  Gunjan Soni,et al.  Signed visibility graphs of time series and their application to brain networks , 2019 .

[35]  Leandro Tortosa,et al.  A variant of the current flow betweenness centrality and its application in urban networks , 2019, Appl. Math. Comput..

[36]  Lucas Lacasa,et al.  Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks , 2017, bioRxiv.