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Real-time particle pollution sensing using machine learning

Real-time particle pollution sensing using machine learning
Real-time particle pollution sensing using machine learning
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.
1094-4087
27237-27246
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
MacKay, Benita S.
318d298f-5b38-43d7-b30d-8cd07f69acd4
Baker, James
7e0eeedd-d5af-43d5-be73-8220f4a28629
Heath, Daniel J.
d53c269d-90d2-41e6-aa63-a03f8f014d21
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
MacKay, Benita S.
318d298f-5b38-43d7-b30d-8cd07f69acd4
Baker, James
7e0eeedd-d5af-43d5-be73-8220f4a28629
Heath, Daniel J.
d53c269d-90d2-41e6-aa63-a03f8f014d21
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, MacKay, Benita S., Baker, James, Heath, Daniel J., Xie, Yunhui, Loxham, Matthew, Eason, Robert W. and Mills, Ben (2018) Real-time particle pollution sensing using machine learning. Optics Express, 26 (21), 27237-27246. (doi:10.1364/OE.26.027237).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 11 September 2018
e-pub ahead of print date: 3 October 2018
Published date: 15 October 2018

Identifiers

Local EPrints ID: 425056
URI: http://eprints.soton.ac.uk/id/eprint/425056
ISSN: 1094-4087
PURE UUID: f6e24ab5-5f39-4bd8-a273-e6c4553c99e7
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benita S. MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Matthew Loxham: ORCID iD orcid.org/0000-0001-6459-538X
ORCID for Robert W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Ben Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 09 Oct 2018 16:30
Last modified: 18 Feb 2021 17:37

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