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Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibra...

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Autores principales: Sousan, Sinan, Gray, Alyson, Zuidema, Christopher, Stebounova, Larissa, Thomas, Geb, Koehler, Kirsten, Peters, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163282/
https://www.ncbi.nlm.nih.gov/pubmed/30205550
http://dx.doi.org/10.3390/s18093008
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author Sousan, Sinan
Gray, Alyson
Zuidema, Christopher
Stebounova, Larissa
Thomas, Geb
Koehler, Kirsten
Peters, Thomas
author_facet Sousan, Sinan
Gray, Alyson
Zuidema, Christopher
Stebounova, Larissa
Thomas, Geb
Koehler, Kirsten
Peters, Thomas
author_sort Sousan, Sinan
collection PubMed
description Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.
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spelling pubmed-61632822018-10-10 Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network Sousan, Sinan Gray, Alyson Zuidema, Christopher Stebounova, Larissa Thomas, Geb Koehler, Kirsten Peters, Thomas Sensors (Basel) Article Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field. MDPI 2018-09-08 /pmc/articles/PMC6163282/ /pubmed/30205550 http://dx.doi.org/10.3390/s18093008 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sousan, Sinan
Gray, Alyson
Zuidema, Christopher
Stebounova, Larissa
Thomas, Geb
Koehler, Kirsten
Peters, Thomas
Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network
title Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network
title_full Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network
title_fullStr Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network
title_full_unstemmed Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network
title_short Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network
title_sort sensor selection to improve estimates of particulate matter concentration from a low-cost network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163282/
https://www.ncbi.nlm.nih.gov/pubmed/30205550
http://dx.doi.org/10.3390/s18093008
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