  This paper explores the topic of preferential sampling, specifically
situations where monitoring sites in environmental networks are preferentially
located by the designers. This means the data arising from such networks may
not accurately characterize the spatio-temporal field they intend to monitor.
Approaches that have been developed to mitigate the effects of preferential
sampling in various contexts are reviewed and, building on these approaches, a
general framework for dealing with the effects of preferential sampling in
environmental monitoring is proposed. Strategies for implementation are
proposed, leading to a method for improving the accuracy of official statistics
used to report trends and inform regulatory policy. An essential feature of the
method is its capacity to learn the preferential selection process over time
and hence to reduce bias in these statistics. Simulation studies suggest
dramatic reductions in bias are possible. A case study demonstrates use of the
method in assessing the levels of air pollution due to black smoke in the UK
over an extended period (1970-1996). In particular, dramatic reductions in the
estimates of the number of sites out of compliance are observed.
