Literature DB >> 31102872

Can complex networks describe the urban and rural tropospheric O3 dynamics?

Rafael Carmona-Cabezas1, Javier Gómez-Gómez2, Ana B Ariza-Villaverde2, Eduardo Gutiérrez de Ravé2, Francisco J Jiménez-Hornero2.   

Abstract

Tropospheric ozone (O3) time series have been converted into complex networks through the recent so-called Visibility Graph (VG), using the data from air quality stations located in the western part of Andalusia (Spain). The aim is to apply this novel method to differentiate the behavior between rural and urban regions when it comes to the ozone dynamics. To do so, some centrality parameters of the resulting complex networks have been investigated: the degree, betweenness and shortest path. Some of them are expected to corroborate previous works in order to support the use of this technique; while others to supply new information. Results coincide when describing the difference that tropospheric ozone exhibits seasonally and geographically. It is seen that ozone behavior is fractal, in accordance to previous works. Also, it has been demonstrated that this methodology is able to characterize the divergence encountered between measurements in urban environments and countryside. In addition to that, the promising outcomes of this technique support the use of complex networks for the study of air pollutants dynamics. Particularly, new nuances are offered such as the identification and description of singularities in the signal.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords:  Centrality measures; Complex networks; Time series; Tropospheric ozone; Visibility graphs

Mesh:

Substances:

Year:  2019        PMID: 31102872     DOI: 10.1016/j.chemosphere.2019.05.057

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  4 in total

1.  Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China's Three Urban Agglomerations as Examples.

Authors:  Feipeng Guo; Zifan Wang; Shaobo Ji; Qibei Lu
Journal:  Int J Environ Res Public Health       Date:  2022-04-07       Impact factor: 4.614

2.  Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs.

Authors:  Javier Gómez-Gómez; Rafael Carmona-Cabezas; Elena Sánchez-López; Eduardo Gutiérrez de Ravé; Francisco José Jiménez-Hornero
Journal:  Entropy (Basel)       Date:  2021-02-08       Impact factor: 2.524

3.  Spatiotemporal heterogeneity and influencing factors on urbanization and eco-environment coupling mechanism in China.

Authors:  Wenxia Zeng; Xi Chen; Qirui Wu; Huizhong Dong
Journal:  Environ Sci Pollut Res Int       Date:  2022-08-04       Impact factor: 5.190

4.  Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network.

Authors:  Zhe Zhang; Hong-Di He; Jin-Ming Yang; Hong-Wei Wang; Yu Xue; Zhong-Ren Peng
Journal:  Chemosphere       Date:  2022-01-15       Impact factor: 7.086

  4 in total

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