1 3 Storage Partners: University of Helsinki, City of Helsinki, Forum Virium, Elisa Pilot project for MegaSense funded by the City of Helsinki. Investigate the use of new technologies (5G) for observing urban environments Current activities: 50 low-cost AQ sensors in Kumpula area Cellular IoT (NB-IoT) for continuous monitoring ML-based sensor calibration to improve accuracy of low-cost sensors Hyperspectral imaging to monitor AQ SMEAR tower in MegaSense platform at the University of Helsinki Kumpula API for companies to develop AQM solutions Partners: City of Helsinki, University of Helsinki (INAR, Computer Science, Geosciences), Forum Virium Helsinki, Vaisala, HSY, Useless and FMI. HOPE (Healthy Outdoor Premises for Everyone) is funded by European Union Urban Innovative Actions (UIA) programme. Participatory AQ sensing and interventions in Helsinki area Current activities: 100 portable University of Helsinki sensors developed by the UH CS dept Campaigns in Vallila-Kumpula, Jätkäsaari and Pakila MegaSense platform connected to FMI ENFUSER Averaged INAR led AQ2.0 concentration in Jätkäsaari Partners: Nokia, University of Helsinki, FMI, Gasera, Metosin (with LoopShore), MetaTavu, Aalto University, City of Helsinki, Elisa. Co-innovation project funded by Business Finland. Current activities: Project just started Planning first PoC Our devices measure: Particulate matter PM2.5 and PM10 Most gases considered by AQI Ozone (O3) Carbon monoxide (CO) Nitrogen dioxide (NO2) Off the shelf components Lot of attention to detail air flow, casing materials Low cost low accuracy?? Colocation experiments Place low cost sensors next to reference equipment In field performance Laboratory studies INAR lab with calibration chamber for gas sensors FMI collaboration for PM sensors Controlled environment What exactly does a sensor measure? Sample results Measurements follow expected pattern of reference data but have noticeable error Accuracy sufficient to indicate air quality status (Good, Fair, Unhealthy) most of the time useful for detecting pollution hotspots Sensors not always measure what spec says (PM) Which factors affect measurements? Meteorological parameters: temp, RH, air pressure, wind Also cross pollutant sensitivities Apply machine learning Idea: leverage correlation between measurement error and meteorological parameters Good results with deep learning Reduces PM2.5 / PM10 error by up to 39% / 55% compared to baseline ML models Enables continuous in-field calibration Feasible to run on Raspberry Pi in real-time How to train models? Different environments (Beijing vs. Helsinki) Need long enough training data sets PM 2.5 May need fine tuning after a while E.g. saturation of sensitive material How to detect that model no longer works? Effect of recalibration distance and duration? Motion & AQ in a smart office environment Ubicampus equipped with infrastructure- based motion sensors Low cost AQ sensing devices Can motion sensors capture variations in indoor air quality? Clear connection between PM2.5 and motion No relationship to CO2 can be established need deeper understanding of human activity within the space Extend later to include VOCs Wearable personal air pollutant exposure monitoring Low cost sensing devices Different transportation modalities, geographic areas, locations within a vehicle Personal exposure and ingestion of pollutants Lessons learned so far Wearable AQ monitoring can identify variations in air quality across different transportation activities Envir onment Duration (min) Distance (km) DD ( ) DDd ( / km) Bus 40 14.6 5.64 0.38 Applicable for transport mode selection, seat Train 30 25.6 1.31 0.05 mapping for most vulnerable people etc. Metro 42 26.1 2.46 0.09 Tram 60 16.8 8.3 0.49 Ferry 60 10.4 12.1 1.16 Roadside 40 2.3 31 13.47 .