An IoT based approach to ultra high resolution air quality mapping thorigh field calibrated monitoring devices

Recent advances in IoT and chemical sensors calibration technologies have led to the proposal of Hierarchical air quality monitoring networks. They are indeed complex systems relying on sensing nodes which differs from size, cost, accuracy, technology, maintenance needs while having the potential to empower smart cities and communiities with increased knowledge  on the highly spatiotemporal variance Air Quality phenomenon (see [1]). The AirHeritage project, funded by Urban Innovative Action program have developed and implemented a hierarchical monitoring system which allows for offering real time assessments and model based forecasting services including 7 fixed low cost sensors station, one (mobile and temporary located) regulatory grade analyzer and a citizen science based ultra high resolution AQ mapping tool based on field calibrated mobile analyzers. This work will analyze the preliminary results of the project by focusing on the machine learning driven sensors calibration methodology and citizen science based air quality mapping campaigns. Thirty chemical and particulate matter multisensory devices have been deployed in Portici, a 4Km2 city located 7 km south of Naples which is  affected by significant car traffic. The devices have been  entrusted to local citizens association for implementing 1 preliminary validation campaign (see [2]) and 3 opportunistic 2-months duration monitoring campaigns. Each 6 months, the devices undergoes a minimum 3 weeks colocation period with a regulatory grade analyzer allowing for training and validation dataset building. Multilinear regression sw components are trained to reach ppb level accuracy (MAE <10ug/m^3 for NO2 and O3, <15ug/M^3 for PM2.5 and PM10, <300ug/M^3 for CO) and encoded in a companion smartphone APP which allows the users for real time assessment of personal exposure. In particular, a novel AQI strongly based on European Air Quality Index ([3]) have been developed for AQ real time data communication. Data have been collected using a custom IoT device management platform entrusted with inception, storage and data-viz roles. Finally data have been used to build UHR (UHR) AQ maps, using spatial binning approach (25mx25m) and median computation for each bin receiving more than 30 measurements during the campaign. The resulting maps have hown the possibility to allow for pinpointing city AQ hotpots which will allows fact-based remediation policies in cities lacking objective technologies to locally assess concentration exposure.  

 

[1] Nuria Castell et Al., Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environment International, Volume 99, 2017, Pages 293-302 ISSN 0160-4120, https://doi.org/10.1016/j.envint.2016.12.007.

[2] De Vito, S, 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

[3] https://airindex.eea.europa.eu/Map/AQI/Viewer/