Dynamic Complex Network Analysis of PM 2.5 Concentrations in the UK, Using Hierarchical Directed Graphs (V1.0.0)

Parya Broomandi, Xueyu Geng, Weisi Guo, Alessio Pagani, David Topping and Jong Ryeol Kim
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Parya Broomandi: School of Engineering, The University of Warwick, Coventry CV4 7AL, UK
Xueyu Geng: School of Engineering, The University of Warwick, Coventry CV4 7AL, UK
Weisi Guo: School of Engineering, The University of Warwick, Coventry CV4 7AL, UK
Alessio Pagani: The Alan Turing Institute, London NW1 2DB, UK
David Topping: The Alan Turing Institute, London NW1 2DB, UK
Jong Ryeol Kim: Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan

Sustainability, 2021, vol. 13, issue 4, 1-14

Abstract: 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 PM 2.5 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 data-driven directed graph representation, to deduce causal directionality and spatial embeddedness. An undirected correlation and a directed Granger causality network were established through utilizing PM 2.5 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 high-dimensional knowledge in a causal inference and stability framework.

Keywords: atmospheric pollution; causality; stability; complex network; PM 2.5 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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