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.

翻译：大气污染是全球主要的致死因素之一，每年导致数百万人死亡。高效监测对于评估暴露水平和执行法定限值至关重要。新型低成本传感器可在更广范围和更多样化地点部署，这使得高效自动化布置问题尤为突出。先前研究表明贝叶斯优化是合适的方法，但仅考虑了卫星数据集，且数据汇总于所有海拔高度。人类呼吸的地表污染才是最重要的。我们采用层次模型改进了现有结果，并在伦敦城市污染数据上评估模型，证明贝叶斯优化可成功应用于该问题。