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Worldwide exposure to fine atmospheric particles can exasperate the risk of awide range of heart and respiratory diseases, due to their ability to penetratedeep into the lungs and blood streams. Epidemiological studies in Europe andelsewhere have established the evidence base pointing to the important role ofPM2.5 in causing over 4 million deaths per year. Traditional approaches tomodel atmospheric transportation of particles suffer from high dimensionalityfrom both transport and chemical reaction processes, making multi-sale causalinference challenging. We apply alternative model reduction methods: adata-driven directed graph representation to infer spatial embeddedness andcausal directionality. Using PM2.5 concentrations in 14 UK cities over a 12month period, we construct an undirected correlation and a directed Grangercausality network. We show for both reduced-order cases, the UK is divided intotwo a northern and southern connected city communities, with greater spatialembedding in spring and summer. We go on to infer stability to disturbances viathe network trophic coherence parameter, whereby we found that winter had thegreatest vulnerability. As a result of our novel graph-based reduced modeling,we are able to represent high-dimensional knowledge into a causal inference andstability framework.

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