Bayesian Optimisation for Active Monitoring of Air Pollution

Sigrid Passano Hellan, Christopher G. Lucas, Nigel H. Goddard

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence
Subtitle of host publicationVol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
Place of PublicationPalo Alto, California, USA
PublisherAssociation for the Advancement of Artificial Intelligence AAAI
Number of pages13
ISBN (Electronic)978-1-57735-876-3, 1-57735-876-7
Publication statusPublished - 28 Jun 2022
Event36th AAAI Conference on Artificial Intelligence - Virtual Conference
Duration: 22 Feb 20221 Mar 2022
Conference number: 36

Publication series

NameThirty-Sixth AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


Conference36th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2022
Internet address


  • AI For Social Impact


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