Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance
Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes,and highlighting their limitations and improvements. Robust linear models using sensor reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM2.5 background concentration of 5.5 μg/m3, demonstrating that, with careful calibration, PM-LCS may cost effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.
Air pollution, Calibation, Low-cost sensors, Machine learning, PM2.5, Particulate matter
Bulot, Florentin M.J.
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Ossont, Steven J.
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Morris, Andrew K.R.
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Basford, Philip J.
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Easton, Natasha H.C.
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Mitchell, Hazel L.
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Foster, Gavin L.
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Cox, Simon J.
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Loxham, Matthew
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May 2023
Bulot, Florentin M.J.
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Ossont, Steven J.
6b903ec2-7bae-4a56-9c21-eea0a70bfa2b
Morris, Andrew K.R.
00ef3bfb-219c-4ea7-86d2-dd4d71c083a6
Basford, Philip J.
efd8fbec-4a5f-4914-bf29-885b7f4677a7
Easton, Natasha H.C.
56583bc7-b005-40d8-8571-99d335430a8f
Mitchell, Hazel L.
06b74ff6-e3ef-469f-8a69-b31ed409c09b
Foster, Gavin L.
fbaa7255-7267-4443-a55e-e2a791213022
Cox, Simon J.
0e62aaed-24ad-4a74-b996-f606e40e5c55
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Bulot, Florentin M.J., Ossont, Steven J., Morris, Andrew K.R., Basford, Philip J., Easton, Natasha H.C., Mitchell, Hazel L., Foster, Gavin L., Cox, Simon J. and Loxham, Matthew
(2023)
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance.
Heliyon, 9 (5), [e15943].
(doi:10.1016/j.heliyon.2023.e15943).
Abstract
Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes,and highlighting their limitations and improvements. Robust linear models using sensor reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM2.5 background concentration of 5.5 μg/m3, demonstrating that, with careful calibration, PM-LCS may cost effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.
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PIIS240584402303150X
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Submitted date: 13 January 2023
Accepted/In Press date: 27 April 2023
e-pub ahead of print date: 29 April 2023
Published date: May 2023
Additional Information:
Funding Information:
National Institute for Health Research Southampton Biomedical Research Centre Senior Research Fellowship, and Biotechnology and Biological Research Council David Phillips Fellowship BB/V004573/1 (ML)
Funding Information:
This work was supported by: Engineering and Physical Sciences Research Council UK grant EP/T517859/1 (FMJB).
Funding Information:
This work was supported by: Engineering and Physical Sciences Research Council UK grant EP/T517859/1 (FMJB).Next Generation Unmanned Systems Science Centre for Doctoral Training supported by the Natural Environment Research Council UK grant NE/N012070/1 (FMJB).Next Generation Unmanned Systems Science Centre Capital Grant 2018 and 2019 (FMJB, SO, GLF, SJC, ML, AKRM).Leverhulme Trust through the Southampton Marine and Maritime Institute (FMJB)Higher Education Innovation Funding (HEIF) from HEFCE to the Southampton Marine & Maritime Institute (SMMI), University of Southampton (SO, FMJB, PJB, NHCE, ML, AKRM, GLF, SJC).National Institute for Health Research Southampton Biomedical Research Centre Senior Research Fellowship, and Biotechnology and Biological Research Council David Phillips Fellowship BB/V004573/1 (ML)
Funding Information:
Next Generation Unmanned Systems Science Centre for Doctoral Training supported by the Natural Environment Research Council UK grant NE/N012070/1 (FMJB).
Publisher Copyright:
© 2023 The Authors
Keywords:
Air pollution, Calibation, Low-cost sensors, Machine learning, PM2.5, Particulate matter
Identifiers
Local EPrints ID: 482725
URI: http://eprints.soton.ac.uk/id/eprint/482725
ISSN: 2405-8440
PURE UUID: 07df20e4-a21b-4e5f-9fd0-e9378129d466
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Date deposited: 12 Oct 2023 16:35
Last modified: 18 Mar 2024 03:28
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Contributors
Author:
Florentin M.J. Bulot
Author:
Andrew K.R. Morris
Author:
Natasha H.C. Easton
Author:
Hazel L. Mitchell
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