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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-342
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-342
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 05 Mar 2020

Submitted as: model description paper | 05 Mar 2020

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This preprint is currently under review for the journal GMD.

Dynamic Complex Network Analysis of PM2.5 Concentrations in the UK using Hierarchical Directed Graphs (V1.0.0)

Parya Broomandi1,2,6, Xueyu Geng1, Weisi Guo3,1,4, Jong Ryeol Kim2, Alessio Pagani4, and David Topping5,4 Parya Broomandi et al.
  • 1School of Engineering, The University of Warwick, Coventry, CV4 7AL, UK
  • 2Department of Civil and Environmental Engineering, Nazarbayev University, 010000, Astana, Kazakhstan
  • 3School of Aerospace, Transport, and Manufacturing, Cranfield University, Bedford, MK43 0AL, UK
  • 4The Alan Turing Institute, London, UK
  • 5School of Earth, Atmospheric and Environmental Science, University of Manchester, Manchester M13 9PL, UK
  • 6Department of Chemical Engineering, Masjed-SoleimanBranch, Islamic Azad University, Masjed-Soleiman, Iran

Abstract. Worldwide exposure to fine atmospheric particles can exasperate the risk of a wide range of heart and respiratory diseases, due to their ability to penetrate deep into the lungs and bloodstreams. Epidemiological studies in Europe and elsewhere have established the evidence base pointing to the important role of PM2.5 (fine particles with a diameter of 2.5 microns or less) in causing over 4 million deaths per year. Traditional approaches to model atmospheric transportation of particles suffer from high dimensionality from both transport and chemical reaction processes, making multi-sale causal inference challenging. We apply alternative model reduction methods – a data-driven directed graph representation to infer spatial embeddedness and causal directionality. Using PM2.5 concentrations in 14 UK cities over a 12-month period, we construct an undirected correlation and a directed Granger causality network. We show for both reduced-order cases, the UK is divided into two northern and southern connected city communities, with greater spatial embedding in spring and summer. We go on to infer stability to disturbances via the network trophic coherence parameter, whereby we found that winter had the greatest vulnerability. As a result of our novel graph-based reduced modeling, we are able to represent high-dimensional knowledge into a causal inference and stability framework.

Parya Broomandi et al.

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Latest update: 28 Mar 2020
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As a result of our novel graph-based reduced modeling, we are able to represent high-dimensional knowledge into a causal inference and stability framework.
As a result of our novel graph-based reduced modeling, we are able to represent high-dimensional...
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