  Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost
chemical microsensors array that recently have showed capable to provide
relatively accurate air pollutant quantitative estimations. Their availability
permits to deploy pervasive Air Quality Monitoring (AQM) networks that will
solve the geographical sparseness issue that affect the current network of AQ
Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown
limited in long term field deployments due to negative influence of several
technological issues including sensors poisoning or ageing, non target gas
interference, lack of fabrication repeatability, etc. Seasonal changes in
probability distribution of priors, observables and hidden context variables
(i.e. non observable interferents) challenge field data driven calibration
models which short to mid term performances recently rose to the attention of
Urban authorithies and monitoring agencies. In this work, we address this non
stationary framework with adaptive learning strategies in order to prolong the
validity of multisensors calibration models enabling continuous learning.
Relevant parameters influence in different network and note-to-node
recalibration scenario is analyzed. Results are hence useful for pervasive
deployment aimed to permanent high resolution AQ mapping in urban scenarios as
well as for the use of AQMS as AQRMS backup systems providing data when AQRMS
data are unavailable due to faults or scheduled mainteinance.
