Sam De Craemer, Jordy Vercauteren, Frans Fierens, Wouter Lefebvre, Filip Meysman

Citizen science projects that monitor air quality have recently drastically expanded in scale. Projects involving thousands of citizens generate spatially dense datasets using low-cost passive samplers for nitrogen dioxide (NO2), which complement data from the sparse reference network operated by environmental agencies. However, there is a critical bottleneck in using these citizen-derived datasets for air quality policy. Passive samplers typically determine the average concentration over a time span of only a few weeks, and this time-limited character of the monitoring effort prohibits policy use, as compliance checking requires annual averaged concentrations, which are not affected by seasonal fluctuations in air quality. Here, we describe a model approach to reliably transform passive sampler NO2 data from multi-week averages to annual averaged values. We verify the assumptions underlying the model procedure, and demonstrate that model uncertainty complies with the EU quality objectives for air quality monitoring. Our approach allows a considerable cost-optimization of passive sampler campaigns and removes a critical bottleneck for citizen-derived data to be used for compliance checking and air quality policy use.