Real-time particle pollution sensing using machine learning

• DOI: 10.1364/oe.26.027237
• Publication Date: 2018-10-03T21:23:04Z
• Authors: James A. Grant-Jacob; Benita S. Mackay; James A. G. Baker; Daniel J. Heath; Yunhui Xie; Matthew Loxham; Robert W. Eason; Ben Mills
• Keywords: 13. Climate action; 02 engineering and technology; 0210 nano-technology; 543; 620

Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.