Citizen-operated mobile low-cost sensors for urban PM2.5 monitoring: field calibration, uncertainty estimation, and application

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 mu m). 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 mu g m(-3). We approximate that one km h(-1) increase in the speed of the bikes will add 0.03-0.04 mu g 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.

Automated summary

Research communities, engagement campaigns, and administrative agents are increasingly using low-cost air quality monitoring technologies. This study assesses the performance of the Snifferbike sensor kit in measuring PM2.5 concentrations in Kristiansand, Norway. The results show the importance of calibrating the mobile sensors and the minimum number of measurements required for data representativeness.