Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds

Abstract

Air quality improved significantly over the past decades. Nevertheless, air pollution continuous to have significant health impacts worldwide. To better assess people's exposure to air pollution, there is a need for higher, more personalized monitoring granularity. IoT sensor technologies can meet these requirements and pave the way towards more fine-grained air quality monitoring, improving our understanding while creating a higher public awareness driving behavioural change. This work tested the validity of scalable PM and NO2 calibration algorithms on various types of sensors (SDS011, OPC-N3, SPS30, NO2-A43F) in five different sensor testbeds deployed at various locations in Belgium and the Netherlands. The calibration models account for sensor gain and offset, while compensating for observed sensitivities of low-cost optical and electrochemical sensors. The calibration improves sensor data considerably (accuracy, linearity and correlation) up to sensitizing and supplementary (EU Class 1) categories at hourly and daily resolutions. Thanks to its cloud implementation and openly available input data, this calibration can be provided "as a service" on top of existing sensor networks in any city, on any sensor. Although distant calibration approaches improve sensor data, the ultimate performance will still depend on the applied sensor type, unit (design of sensor box) and granularity of the available reference monitoring network.