  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.
