The air quality sensor equipment (AirSensa) delivers the right combination of lower cost, good accuracy and great aesthetics. The calibration process and facilities we have developed ensure that we get the data we want to the accuracy we need. The equipment comes in two versions: AirSensa A, which is wired into an electrical supply, and AirSensa B, which is solar-powered. There are necessarily some detailed differences because of the reliability of power delivery, but they are essentially the same unit.
We are working with partners such as Kings College London and Cambridge University on both unit calibration & accuracy and data interpretation.
The units are available as standard in light grey, but as we don't want to add to visual pollution, particularly in central London, site owners can specify any other colour they wish to ensure the units blend into their surroundings.
The cloud software platform that underpins AirSensa (Storrm) is critical for driving the project in every aspect, including estate management, maintenance automation, data visualisation, and consumer apps. The platform integrates every aspect of our operations at very low cost, and automating the key aspects of running a large sensor network, even down to instructing each AirSensa unit in real time how to modify its operation as necessary (particularly important for solar units during long periods of very low light levels for example).
The Storrm platform makes the long-term sustainability of the network possible by reducing maintenance and running costs to a manageable level.
The data we produce is of a quality high enough to be valuable in multiple contexts, at a fraction of the usual cost. Data is curated and maintained centrally on our cloud platform, and will be granular enough to illustrate air quality down to individual street level in most cases.
We are working with partners such as Kings College London and Cambridge University on data processing and interpretation. But importantly we are also working with many groups of people - from communities to technical developers - to ensure that information will be presented in the most useful and consumable ways.
At full deployment, we expect to be producing more than 300Gb of data every month every byte of which require post-processing and analysis - a big data challenge indeed!