Bayesian Optimisation for Active Monitoring of Air Pollution
This work addresses efficient monitoring of ground-level air pollution, which is critical for public health, but is incremental as it builds on existing Bayesian optimisation methods with specific model enhancements.
The paper tackled the problem of efficiently placing air pollution sensors using Bayesian optimisation, improving on previous work by applying hierarchical models to ground-level urban data in London and demonstrating successful application.
Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbers and in more varied locations, motivating the problem of efficient automated placement. Previous work suggests Bayesian optimisation is an appropriate method, but only considered a satellite data set, with data aggregated over all altitudes. It is ground-level pollution, that humans breathe, which matters most. We improve on those results using hierarchical models and evaluate our models on urban pollution data in London to show that Bayesian optimisation can be successfully applied to the problem.