Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine
Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.
A-rapport, A2655