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Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monito...

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Autores principales: Ionascu, Marian-Emanuel, Castell, Nuria, Boncalo, Oana, Schneider, Philipp, Darie, Marius, Marcu, Marius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659498/
https://www.ncbi.nlm.nih.gov/pubmed/34883981
http://dx.doi.org/10.3390/s21237977
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author Ionascu, Marian-Emanuel
Castell, Nuria
Boncalo, Oana
Schneider, Philipp
Darie, Marius
Marcu, Marius
author_facet Ionascu, Marian-Emanuel
Castell, Nuria
Boncalo, Oana
Schneider, Philipp
Darie, Marius
Marcu, Marius
author_sort Ionascu, Marian-Emanuel
collection PubMed
description During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O(3)), and nitrogen dioxide (NO(2)). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O(3) gases and a similar accuracy for the NO(2) gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.
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spelling pubmed-86594982021-12-10 Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring Ionascu, Marian-Emanuel Castell, Nuria Boncalo, Oana Schneider, Philipp Darie, Marius Marcu, Marius Sensors (Basel) Article During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O(3)), and nitrogen dioxide (NO(2)). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O(3) gases and a similar accuracy for the NO(2) gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform. MDPI 2021-11-29 /pmc/articles/PMC8659498/ /pubmed/34883981 http://dx.doi.org/10.3390/s21237977 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ionascu, Marian-Emanuel
Castell, Nuria
Boncalo, Oana
Schneider, Philipp
Darie, Marius
Marcu, Marius
Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
title Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
title_full Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
title_fullStr Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
title_full_unstemmed Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
title_short Calibration of CO, NO(2), and O(3) Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
title_sort calibration of co, no(2), and o(3) using airify: a low-cost sensor cluster for air quality monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659498/
https://www.ncbi.nlm.nih.gov/pubmed/34883981
http://dx.doi.org/10.3390/s21237977
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