LAUSR

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. The monitoring effort typically lasts only a few weeks, while long-term air quality guidelines are based on annual averaged concentrations that are not affected by seasonal fluctuations in air quality. Here, we describe a statistical model approach to reliably transform passive sampler NO2 data from multi-week averages to annual averaged values. The predictive model is trained with data from reference stations that are limited in number but provide full temporal coverage, and is subsequently applied to the one-off dataset recorded by the spatially extensive network of passive samplers. 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.