AQMesh co-location comparison trial results for NO2 in California, USA (V5.0)
|Correlation co-efficient (R2)||0.89|
|AQMesh processing algorithm||V4.2.3|
|Averaged sample interval||1 hour|
All comparison trials are from regularly maintained reference stations. Not all are specifically identified due to the administrative overhead of getting permission to name data sources. We do not always use ratified reference data to avoid delays in analysis but re-running the comparison analysis after data ratification often improves correlation.
AQMesh co-location comparison trial results for NO2 in London, UK (V4.2.3)
AQMesh co-location comparison trial results for NO2 in Slovakia, Europe (V4.2.3)
AQMesh co-location comparison trial results for NO2 in Minnesota, USA (V4.2.3)
AQMesh co-location comparison trial results for PM2.5 in Minnesota, USA (V3.0)
AQMesh co-location comparison trial results for PM2.5 in Halifax, Canada (V3.0h)
Whilst some manufacturers use a calculation to give an NO2 reading, the AQMesh air quality monitoring system uses a unique O3-filtered NO2 sensor for accurate, direct measurement of NO2.
The AQMesh air quality monitor is neat, compact and easy install within just a few minutes and experienced technical staff are not required.
AQMesh air quality monitoring systems can measure just one gas or up to six gases, particles, noise and wind speed & direction. Humidity, pressure and pod temperature are always measured.
With wireless communications using the global mobile network and a choice of power options, including batteries, mains supply or a bespoke solar pack, AQMesh air quality monitors can be installed in almost any location.
With AQMesh outdoor air quality monitors, meaningful correction of cross-gas effects and interference from environmental conditions is consistently delivered through proven and traceable processing.
The carefully developed data processing algorithms used by AQMesh outdoor air quality monitors are fully traceable and fixed by version number, and, unlike other systems, have been achieved with no use of machine learning or artificial intelligence.