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

Sensors

卷:21

Paolo D’Auria¹  Antonio Del Giudice²  Ettore Massera²  Sergio Ferlito²  Girolamo Di Francia²  Maria Salvato²  Gerardo D’Elia²  Fabrizio Formisano²  Elena Esposito²  Grazia Fattoruso²  Saverio De Vito²  Tiziana Polichetti²  Adrian M. Ionescu³ 

[1] ARPA Campania, Via Vicinale Santa Maria del Pianto Centro Polifunzionale, Torre 1, 80143 Napoli, Italy;

[2] ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy;

[3] NanoLab, EPFL-Ecole Politechnique Federal de Lausanne, 1015 Lausanne, Switzerland;

关键词: IoT AQ nodes;    sensor network;    calibration;    air quality monitoring;    machine learning;   

DOI  :  10.3390/s21155219

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown