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

TL;DR

This paper introduces a novel graph-based approach to analyze PM2.5 pollution in the UK, revealing spatial and seasonal patterns, community structures, and stability vulnerabilities using reduced-order causal networks.

Contribution

It presents a new data-driven graph modeling method for high-dimensional atmospheric data, enabling causal inference and stability analysis of air pollution patterns.

Findings

UK divided into northern and southern communities

Greater spatial embedding observed in spring and summer

Winter shows highest network vulnerability

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 blood streams. Epidemiological studies in Europe and elsewhere have established the evidence base pointing to the important role of PM2.5 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 a 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.