Bayesian Optimisation for Active Monitoring of Air Pollution

TL;DR

This paper applies Bayesian optimisation with hierarchical models to efficiently place low-cost sensors for ground-level urban air pollution monitoring, demonstrating improved results on London data.

Contribution

It introduces hierarchical Bayesian models for sensor placement and evaluates their effectiveness on real urban pollution data, advancing automated air quality monitoring methods.

Findings

Bayesian optimisation effectively improves sensor placement.

Hierarchical models outperform simpler approaches.

Successful application to London urban pollution data.

Abstract

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.