# Bayesian Optimisation for Active Monitoring of Air Pollution

arXiv: https://arxiv.org/abs/2202.07595 · HTML: https://mlsanity.com/paper/2202.07595/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Approximate Inference for Fully Bayesian Gaussian Process Regression](https://mlsanity.com/paper/1912.13440/) (2019)
- [Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs](https://mlsanity.com/paper/1703.04211/) (2017)
- [How Bayesian Should Bayesian Optimisation Be?](https://mlsanity.com/paper/2105.00894/) (2021)
- [Optimising Placement of Pollution Sensors in Windy Environments](https://mlsanity.com/paper/2012.10770/) (2020)
- [Active Localization of Gas Leaks using Fluid Simulation](https://mlsanity.com/paper/1901.09608/) (2019)
- [Machine Learning for a Low-cost Air Pollution Network](https://mlsanity.com/paper/1911.12868/) (2019)

## Built on by (most cited first, 5 of 5)

- [Transition Constrained Bayesian Optimization via Markov Decision Processes](https://mlsanity.com/paper/2402.08406/) (2024)
- [Bayesian Optimisation Against Climate Change: Applications and Benchmarks](https://mlsanity.com/paper/2306.04343/) (2023)
- [Data-driven Prior Learning for Bayesian Optimisation](https://mlsanity.com/paper/2311.14653/) (2023)
- [Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments](https://mlsanity.com/paper/2309.05784/) (2023)
- [The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs](https://mlsanity.com/paper/2410.14533/) (2024)

## Abstract (arXiv)

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.