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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8124547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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