  Scalable and effective calibration is a fundamental requirement for Low Cost
Air Quality Monitoring Systems and will enable accurate and pervasive
monitoring in cities. Suffering from environmental interferences and
fabrication variance, these devices need to encompass sensors specific and
complex calibration processes for reaching a sufficient accuracy to be deployed
as indicative measurement devices in Air Quality (AQ) monitoring networks.
Concept and sensor drift often force calibration process to be frequently
repeated. These issues lead to unbearable calibration costs which denies their
massive deployment when accuracy is a concern. In this work, We propose a zero
transfer samples, global calibration methodology as a technological enabler for
IoT AQ multisensory devices which relies on low cost Particulate Matter (PM)
sensors. This methodology is based on field recorded responses from a limited
number of IoT AQ multisensors units and machine learning concepts and can be
universally applied to all units of the same type. A multi season test campaign
shown that, when applied to different sensors, this methodology performances
match those of state of the art methodology which requires to derive different
calibration parameters for each different unit. If confirmed, these results
show that, when properly derived, a global calibration law can be exploited for
a large number of networked devices with dramatic cost reduction eventually
allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore,
this calibration model could be easily embedded on board of the device or
implemented on the edge allowing immediate access to accurate readings for
personal exposure monitor applications as well as reducing long range data
transfer needs.
