Cities worldwide are pioneering the use of air quality monitoring stations to diagnose and understand levels of pollution in densely populated areas. But although they can gather substantial volumes of data, a sparse network of large devices often lacks the granular detail to make decisions at a district or neighbourhood level where people are most affected.
AirNode and AirIntel are designed to help take these initiatives to the next stage, adding a layer of ‘street-level’ detail to the data gathered by existing reference stations. These dense networks of high-fidelity, low-cost sensors, backed up by real-time analysis, deliver actionable intelligence that can be used by local authorities, community groups and businesses across multiple settings from traffic flow management to enforcement of clean air legislation.
As data is gathered over longer periods of time it can be used as the basis for clean air strategies, investment in public transport and the identification of less polluted routes for pedestrians.
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This deployment of a dense AirNode sensor network is helping the authority to fully understand air quality in an urban centre. The project successfully attributed air pollution to vehicular traffic while observing long range pollution and identifying specific locations for mitigation solutions.
This ‘before and after’ assessment explored the pollution reduction in a residential street following its closure to traffic. The project was funded by Brent’s Neighbourhood Community Infrastructure Levy (NCIL).
Using its design expertise, AirLabs helped deploy an air quality monitoring and cleaning system that blended in with the existing infrastructure at a major transport hub, improving air quality and helping to encourage people back to public transport.
AirLabs has conducted a number of co-location studies to validate the accuracy of its AirNode sensor by installing devices close to reference stations and comparing the results. In all studies, the AirNode sensors tracked pollution measurements in line with the reference stations proving their accuracy .