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Article

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

1
Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
2
Telenor Research, 1360 Fornebu, Norway
3
Centre for Research and Technology Hellas, Information Technology Institute, 57001 Thermi, Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Hsi-Jen James Yeh
Sensors 2021, 21(9), 3190; https://doi.org/10.3390/s21093190
Received: 15 April 2021 / Revised: 27 April 2021 / Accepted: 30 April 2021 / Published: 5 May 2021
(This article belongs to the Special Issue IoT Application for Smart Cities)
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. View Full-Text
Keywords: air quality; low-cost sensors; sensor calibration; warning systems; data visualization air quality; low-cost sensors; sensor calibration; warning systems; data visualization
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MDPI and ACS Style

Veiga, T.; Munch-Ellingsen, A.; Papastergiopoulos, C.; Tzovaras, D.; Kalamaras, I.; Bach, K.; Votis, K.; Akselsen, S. From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development. Sensors 2021, 21, 3190. https://doi.org/10.3390/s21093190

AMA Style

Veiga T, Munch-Ellingsen A, Papastergiopoulos C, Tzovaras D, Kalamaras I, Bach K, Votis K, Akselsen S. From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development. Sensors. 2021; 21(9):3190. https://doi.org/10.3390/s21093190

Chicago/Turabian Style

Veiga, Tiago, Arne Munch-Ellingsen, Christoforos Papastergiopoulos, Dimitrios Tzovaras, Ilias Kalamaras, Kerstin Bach, Konstantinos Votis, and Sigmund Akselsen. 2021. "From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development" Sensors 21, no. 9: 3190. https://doi.org/10.3390/s21093190

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