A case study in preferential sampling: Long term
monitoring of air pollution in the UK
The effects of air pollution are a major concern both in terms of
the environment and human health. The majority of information
relating to concentrations of air pollution comes from monitoring
networks, data from which are used to inform regulatory criteria
and in assessing health effects. In the latter case, measurements
from the network are interpreted as being representative of levels
to which populations are exposed. However there is the possibility
of selection bias if monitoring sites are located in only the most
polluted areas, a concept referred to as preferential sampling.
Here we examine long-term changes in levels of air pollution
from a monitoring network in the UK which was operational
from the 1960s until 2006. During this unique period in history,
concentrations fell dramatically from levels which would be
unrecognisable in the UK today, reflecting changes in the large scale
use of fossil fuels. As levels fell the network itself was subject to
considerable change. We use spatio-temporal models, set within a
Bayesian framework using INLA for inference, to model declining
concentrations in relation to changes in the network. The results
support the hypothesis of preferential sampling that has largely
been ignored in environmental risk analysis.
Crown Copyright © 2014 Published by Elsevier B.V. All rights
reserved.
Article history:
Received 10 September 2013
Accepted 31 March 2014
Available online 12 April 2014