Optimising Placement of Pollution Sensors in Windy Environments

TL;DR

This paper introduces wind-informed kernels for Bayesian optimisation to improve the placement of pollution sensors in windy environments, aiming to enhance monitoring efficiency and accuracy.

Contribution

It proposes two novel wind-aware kernels for Bayesian optimisation, addressing the limitation of traditional kernels that ignore pollution propagation due to wind.

Findings

Wind-informed kernels outperform traditional kernels in sensor placement tasks

Enhanced active learning of pollution hotspots in windy conditions

Improved efficiency in air pollution monitoring

Abstract

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.