Dynamic complex network analysis of PM2.5 concentrations in the uk, using hierarchical directed graphs (V1.0.0)
Broomandi P. Geng X. Guo W. Pagani A. Topping D. Kim J.R.
2 February 2021MDPI AG
Sustainability (Switzerland)
2021#13Issue 41 - 14 pp.
The risk of a broad range of respiratory and heart diseases can be increased by widespread exposure to fine atmospheric particles on account of their capability to have a deep penetration into the blood streams and lung. Globally, studies conducted epidemiologically in Europe and elsewhere provided the evidence base indicating the major role of PM2.5 leading to more than four million deaths annually. Conventional approaches to simulate atmospheric transportation of particles having high dimensionality from both transport and chemical reaction process make exhaustive causal inference difficult. Alternative model reduction methods were adopted, specifically a datadriven directed graph representation, to deduce causal directionality and spatial embeddedness. An undirected correlation and a directed Granger causality network were established through utilizing PM2.5 concentrations in 14 United Kingdom cities for one year. To demonstrate both reduced-order cases, the United Kingdom was split up into two southern and northern connected city communities, with notable spatial embedding in summer and spring. It continued to reach stability to disturbances through the network trophic coherence parameter and by which winter was construed as the most considerable vulnerability. Thanks to our novel graph reduced modeling, we could represent highdimensional knowledge in a causal inference and stability framework.
Atmospheric pollution , Causality , Complex network , PM2.5 , Stability
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School of Engineering, The University of Warwick, Coventry, CV4 7AL, United Kingdom
Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Department of Chemical Engineering, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran
School of Aerospace, Transport, and Manufacturing, Cranfield University, Bedford, MK43 0AL, United Kingdom
The Alan Turing Institute, London, NW1 2DB, United Kingdom
School of Earth, Atmospheric and Environmental Science, University of Manchester, Manchester, M13 9PL, United Kingdom
School of Engineering
Department of Civil and Environmental Engineering
Department of Chemical Engineering
School of Aerospace
The Alan Turing Institute
School of Earth
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