  Air pollution is one of the most important causes of mortality in the world.
Monitoring air pollution is useful to learn more about the link between health
and pollutants, and to identify areas for intervention. Such monitoring is
expensive, so it is important to place sensors as efficiently as possible.
Bayesian optimisation has proven useful in choosing sensor locations, but
typically relies on kernel functions that neglect the statistical structure of
air pollution, such as the tendency of pollution to propagate in the prevailing
wind direction. We describe two new wind-informed kernels and investigate their
advantage for the task of actively learning locations of maximum pollution
using Bayesian optimisation.
