Preprints
https://doi.org/10.5194/gmd-2019-342
https://doi.org/10.5194/gmd-2019-342
Submitted as: model description paper
 | 
05 Mar 2020
Submitted as: model description paper |  | 05 Mar 2020
Status: this preprint was under review for the journal GMD but the revision was not accepted.

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

Parya Broomandi, Xueyu Geng, Weisi Guo, Jong Ryeol Kim, Alessio Pagani, and David Topping

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Parya Broomandi, Xueyu Geng, Weisi Guo, Jong Ryeol Kim, Alessio Pagani, and David Topping
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Parya Broomandi, Xueyu Geng, Weisi Guo, Jong Ryeol Kim, Alessio Pagani, and David Topping
Parya Broomandi, Xueyu Geng, Weisi Guo, Jong Ryeol Kim, Alessio Pagani, and David Topping

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Short summary
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.