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From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to...

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Autores principales: Veiga, Tiago, Munch-Ellingsen, Arne, Papastergiopoulos, Christoforos, Tzovaras, Dimitrios, Kalamaras, Ilias, Bach, Kerstin, Votis, Konstantinos, Akselsen, Sigmund
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124547/
https://www.ncbi.nlm.nih.gov/pubmed/34062961
http://dx.doi.org/10.3390/s21093190
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author Veiga, Tiago
Munch-Ellingsen, Arne
Papastergiopoulos, Christoforos
Tzovaras, Dimitrios
Kalamaras, Ilias
Bach, Kerstin
Votis, Konstantinos
Akselsen, Sigmund
author_facet Veiga, Tiago
Munch-Ellingsen, Arne
Papastergiopoulos, Christoforos
Tzovaras, Dimitrios
Kalamaras, Ilias
Bach, Kerstin
Votis, Konstantinos
Akselsen, Sigmund
author_sort Veiga, Tiago
collection PubMed
description Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.
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spelling pubmed-81245472021-05-17 From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development Veiga, Tiago Munch-Ellingsen, Arne Papastergiopoulos, Christoforos Tzovaras, Dimitrios Kalamaras, Ilias Bach, Kerstin Votis, Konstantinos Akselsen, Sigmund Sensors (Basel) Article Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality. MDPI 2021-05-05 /pmc/articles/PMC8124547/ /pubmed/34062961 http://dx.doi.org/10.3390/s21093190 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
Veiga, Tiago
Munch-Ellingsen, Arne
Papastergiopoulos, Christoforos
Tzovaras, Dimitrios
Kalamaras, Ilias
Bach, Kerstin
Votis, Konstantinos
Akselsen, Sigmund
From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
title From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
title_full From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
title_fullStr From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
title_full_unstemmed From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
title_short From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
title_sort from a low-cost air quality sensor network to decision support services: steps towards data calibration and service development
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124547/
https://www.ncbi.nlm.nih.gov/pubmed/34062961
http://dx.doi.org/10.3390/s21093190
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