Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks

Future air quality monitoring networks will include fleets of low-cost gas and particulate matter sensors calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of losses in data quality in operational scenarios. This work focuses on addressing a low-cost NO2 sensor calibration model update triggered via a concept drift detector. This study defines which data are most appropriate for use in the model updating process in order to maintain compliance with the relative expanded uncertainty (REU) limits established by the European Directive, as well as evaluate the potential of general and importance-weighted calibration models in the mitigation of concept drift effects.