COVID19 Pandemic impacts have been associated with Air Quality (AQ) with its mortality index being significantly affected by high pollution levels. Significant mobility limitations have contributed to slow down the pandemic in Italy having as a side effect a definite decrease of pollution levels. Phase 2 while easing those limits still see a significant reduction of commuting and schools related car traffic emissions. High resolution AQ monitoring can now allow to obtain a picture of AQ if smart cities will be capable to reach long sought emissions goals. Furthermore, it could support the identification and forecasting of possible future local SARS-COV-2 outbursts. In this work, we present the outcomes of a high resolution AQ monitoring opportunistic campaign. These have been achieved through the deployment of field data driven calibration trough machine learning and a smartphone centered IoT infrastructure, capable to store, visualize and give exposome feedback to monitoring volunteers.
High Resolution Air Quality Monitoring with IoT Intelligent Multisensor devices during COVID-19 Pandemic Phase 2 in Italy
De Vito S.;Esposito E.;D'Elia G.;Del Giudice A.;Fattoruso G.;Ferlito S.;Di Francia G.;Terzini E.
2020-01-01
Abstract
COVID19 Pandemic impacts have been associated with Air Quality (AQ) with its mortality index being significantly affected by high pollution levels. Significant mobility limitations have contributed to slow down the pandemic in Italy having as a side effect a definite decrease of pollution levels. Phase 2 while easing those limits still see a significant reduction of commuting and schools related car traffic emissions. High resolution AQ monitoring can now allow to obtain a picture of AQ if smart cities will be capable to reach long sought emissions goals. Furthermore, it could support the identification and forecasting of possible future local SARS-COV-2 outbursts. In this work, we present the outcomes of a high resolution AQ monitoring opportunistic campaign. These have been achieved through the deployment of field data driven calibration trough machine learning and a smartphone centered IoT infrastructure, capable to store, visualize and give exposome feedback to monitoring volunteers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
