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Volume 21
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10.3390/s21155219
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Article

Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation

by
Saverio De Vito
1,*,
Elena Esposito
1,*,
Ettore Massera
1,
Fabrizio Formisano
1,
Grazia Fattoruso
1,
Sergio Ferlito
1,
Antonio Del Giudice
1,
Gerardo D’Elia
1,
Maria Salvato
1,†,
Tiziana Polichetti
1,
Paolo D’Auria
2,
Adrian M. Ionescu
3 and
Girolamo Di Francia
1
1
ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy
2
ARPA Campania, Via Vicinale Santa Maria del Pianto Centro Polifunzionale, Torre 1, 80143 Napoli, Italy
3
NanoLab, EPFL-Ecole Politechnique Federal de Lausanne, 1015 Lausanne, Switzerland
*
Authors to whom correspondence should be addressed.
†
M.S. is with ENEA DUEE-SIST Div.
Sensors 2021, 21(15), 5219; https://doi.org/10.3390/s21155219
Submission received: 12 July 2021 / Accepted: 20 July 2021 / Published: 31 July 2021
(This article belongs to the Section Sensor Networks)
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Abstract

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.
Keywords: IoT AQ nodes; sensor network; calibration; air quality monitoring; machine learning IoT AQ nodes; sensor network; calibration; air quality monitoring; machine learning

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MDPI and ACS Style

De Vito, S.; Esposito, E.; Massera, E.; Formisano, F.; Fattoruso, G.; Ferlito, S.; Del Giudice, A.; D’Elia, G.; Salvato, M.; Polichetti, T.; et al. Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors 2021, 21, 5219. https://doi.org/10.3390/s21155219

AMA Style

De Vito S, Esposito E, Massera E, Formisano F, Fattoruso G, Ferlito S, Del Giudice A, D’Elia G, Salvato M, Polichetti T, et al. Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors. 2021; 21(15):5219. https://doi.org/10.3390/s21155219

Chicago/Turabian Style

De Vito, Saverio, Elena Esposito, Ettore Massera, Fabrizio Formisano, Grazia Fattoruso, Sergio Ferlito, Antonio Del Giudice, Gerardo D’Elia, Maria Salvato, Tiziana Polichetti, and et al. 2021. "Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation" Sensors 21, no. 15: 5219. https://doi.org/10.3390/s21155219

APA Style

De Vito, S., Esposito, E., Massera, E., Formisano, F., Fattoruso, G., Ferlito, S., Del Giudice, A., D’Elia, G., Salvato, M., Polichetti, T., D’Auria, P., Ionescu, A. M., & Di Francia, G. (2021). Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors, 21(15), 5219. https://doi.org/10.3390/s21155219

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