Deep learning in airborne particulate matter sensing: a review
Deep learning in airborne particulate matter sensing: a review
Airborne particulate matter pollution is a global health problem that affects people from all demographics. To reduce the impact of such pollution and enable mitigation and policy planning, quantifying individuals' exposure to pollution is necessary. To achieve this, effective monitoring of airborne particulates is required, through monitoring of pollution hotspots and sources. Furthermore, since pollution is a global problem, which varies from urban areas to city centres, industrial facilities to inside homes, a variety of sensors might be needed. Current sensing techniques either lack species resolution on a world scale, lack real-time capabilities, or are too expensive or too large for mass deployment. However, recent work using deep learning techniques has expanded the capability of current sensors and allowed the development of new techniques that have the potential for worldwide, species specific, real-time monitoring. Here, it is proposed how deep learning can enable sensor design for the development of small, low-cost sensors for real-time monitoring of particulate matter pollution, whilst unlocking the capability for predicting future particulate events and health inference from particulates, for both individuals and the environment in general.
air pollution, deep learning, particulate matter, sensing
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
5 December 2022
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James A. and Mills, Benjamin
(2022)
Deep learning in airborne particulate matter sensing: a review.
Journal of Physics Communications, 6 (12), [122001].
(doi:10.1088/2399-6528/aca45e).
Abstract
Airborne particulate matter pollution is a global health problem that affects people from all demographics. To reduce the impact of such pollution and enable mitigation and policy planning, quantifying individuals' exposure to pollution is necessary. To achieve this, effective monitoring of airborne particulates is required, through monitoring of pollution hotspots and sources. Furthermore, since pollution is a global problem, which varies from urban areas to city centres, industrial facilities to inside homes, a variety of sensors might be needed. Current sensing techniques either lack species resolution on a world scale, lack real-time capabilities, or are too expensive or too large for mass deployment. However, recent work using deep learning techniques has expanded the capability of current sensors and allowed the development of new techniques that have the potential for worldwide, species specific, real-time monitoring. Here, it is proposed how deep learning can enable sensor design for the development of small, low-cost sensors for real-time monitoring of particulate matter pollution, whilst unlocking the capability for predicting future particulate events and health inference from particulates, for both individuals and the environment in general.
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JPCO_Review_Final_Corrected_Accepted
- Accepted Manuscript
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Grant-Jacob_2022_J._Phys._Commun._6_122001
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Submitted date: 2022
Accepted/In Press date: 22 November 2022
Published date: 5 December 2022
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Funding Information:
This work was funded by the Engineering and Physical Sciences Research Council (grant no. EP/T026197/1). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Keywords:
air pollution, deep learning, particulate matter, sensing
Identifiers
Local EPrints ID: 473209
URI: http://eprints.soton.ac.uk/id/eprint/473209
PURE UUID: a610ec5d-0f8d-4cf3-8020-586ec81547e4
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Date deposited: 12 Jan 2023 17:57
Last modified: 06 Jun 2024 01:48
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Author:
James A. Grant-Jacob
Author:
Benjamin Mills
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