Updated: Jul 24, 2019
We are so excited about our Zephyr® performance that thought we would share our first calibration data from our new units.
In November 2017, EarthSense installed one of the first units off our production line on the AURN station at the University of Leicester.
The AURN is the Automatic Urban And Rural Network. Run by DEFRA, this network is the UK's largest automatic air quality monitoring network (there are 163 currently in operation) and is the main network used for checking if statutory air quality standards and targets are met (e.g. EC Directives) and compliance reporting. The AURN is the de- facto tool for measurement of Air Quality in the UK.
The performance over 3 days clearly demonstrates critical performance for NO2, highly relevant to monitoring in urban environments. The high time resolution of the Zephyr® (sub-minute) units provides substantial additional information and structure over the hourly AURN network.
The above image is the graph showing AURN comparison with our Zephyr unit for NO2. We are very happy with these results which show reliable performance over this first test. Note that the “scatter” around the AURN hourly averages is mostly due to the high time resolution of the sensor picking up individual bits of polluted air, and is not just random noise.
This is evidenced by the top edge (representing polluted plumes) showing more structure than the bottom edge (representing the urban background). When averaged to hourly resolution we get (exceptionally-good) agreement to 2.5ug (1.S.D.) –we are now running a longer calibration/validation campaign.
Above is the hourly average plot too, which shows how well the Zephyr's agree with the AURN hourly average. This one also includes a second unit which we also had on the AURN at the same time.
The message is that performance so far is very good indeed, and we’ll keep you up to date as more data rolls in. Below is one of our Zephyr's in action measuring air quality, one of its strengths is its size and ability to be placed anywhere, and it’s very discrete, as you can see below.